ACCULTURATION AND HEALTH BELIEFS The

Running head: ACCULTURATION AND HEALTH BELIEFS

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The Effects of Acculturation on Second-Generation Immigrants’ Health Beliefs

Robert Crowder, B.S.

A Thesis Submitted to the Graduate Faculty of Simpson University in Partial Fulfillment of the Requirements for the Degree of Master of Arts in Counseling Psychology

Redding, CA

2017

ACCULTURATION AND HEALTH BELIEFS

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ABSTRACT

The Effects of Acculturation on Second-Generation Immigrants’ Health Beliefs Health care agencies, including mental health agencies, are currently attempting to identify how to provide more culturally appropriate care for their clientele. Prior research found that individuals from different immigrant generations, as well as varying levels of acculturation, may display different health behaviors and possess different health outcomes. There is also evidence that beliefs may be passed from generation to generation. Prior to this study there was no research investigating the health beliefs of second-generation immigrants or if those health beliefs are influenced by acculturation. This study aimed to help close this gap in the literature by implementing a cross-sectional survey utilizing an online survey to gather data on general health beliefs and acculturation from survey respondents. There were 206 individuals (N=206) that responded to the survey and 11.7% of the respondents were second-generation immigrants. The results of this study revealed that immigrant generation is weakly, but positively correlated with two items on the external treatment domain of the general health belief scale. Furthermore, mainstream acculturation was also negatively, but weakly, correlated with an item on the external factors domain and one item on the internal control domain. While the findings from this study cannot be generalized due to its limitations, the findings may be beneficial for both physical health professionals and mental health professionals when attempting to provide culturally competent care to patients.


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COMMITTEE IN CHARGE OF CANDIDACY Assistant Professor Ashley Brimager, Ph. D.

_______________________________________ Thesis Chairperson

_______________ Date

Assistant Professor Rebecca Swartzman M.S.N., M. Div., D.N.P.

_______________________________________ Committee Member

Clinical Training Director Reginald Watson Ph. D., LCPC, NCC

_______________________________________ Committee Member

ACCULTURATION AND HEALTH BELIEFS Dedication This thesis is dedicated to all of my family that have emphasized the importance of education as I grew up.

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Acknowledgement I would like to thank the members on my committee, Dr. Ashley Brimager, Dr. Rebecca Swartzman, and Dr. Reginald Watson for the help and support that they have provided throughout the process of this thesis.

I would also like to thank Victoria for helping me comb through the document and all the support she has provided throughout this entire process.


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Table of Contents List of Tables

9

Chapter One: Introduction

17

Immigration and Acculturation

19

Health Beliefs and the Health Belief Model

27

Modifying variables Cues to action

28 28

Perceived benefits

29

Perceived barriers

29

Perceived severity

29

Perceived self-efficacy

30

Perceived susceptibility

30

Predictive ability

30

Cognitive-Behavioral Therapy and Health Beliefs

31

Interaction of Acculturation and Health Beliefs

31

Chapter Two: Literature Review

37

Counseling and Health Beliefs

37

Health Belief Model

39

Acculturation and Health Beliefs

44

Culture Definitions of health

45 45

Biomedical model

46

Biopsychosocial model

46

Fatalistic beliefs and health

47

Other definitions of health

49

Etiology of disease

49

Perceived susceptibility

53

Perceived severity

54

Perceived benefits

54

Perceived barriers

56


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Cues to action

57

Perceived self-efficacy

58

Chapter Three: Methodology

61

Research Design

61

Participants

61

Procedures

63

Measures

65

Vancouver Index of Acculturation

65

Health Belief Questionnaire

67

Statistical analyses Chapter Four: Results

68 71

Descriptive Statistics

71

Correlational Analysis: Immigrant Generation and Health Beliefs

74

Kruskal-Wallis Analysis

75

Post-Hoc Analysis

76

Correlational Analysis: Acculturation Scores and Health Beliefs

77

Kruskal-Wallis: Immigrant Generation and Acculturation Scores

79

Ordinal Regression Analysis: Immigrant Generation, MA, and Health Beliefs

79

Ordinal Regression Analysis: Immigrant Generation, HA, and Health Beliefs

81

Chapter Five: Discussion

83

Correlational Analyses and Regression Analyses

83

Kruskal-Wallis and Post-Hoc Analysis

86

Findings and the Health Belief Model

87

Generalizability of the Study

89

Applications for Practice

90

Implications for Future Research

92

Conclusion

93

References

189

ACCULTURATION AND HEALTH BELIEFS Appendix

8 223


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List of Tables Table 1:

Demographic Descriptive Statistics

95

Table 2:

Sex Frequencies

96

Table 3:

Age Groups

96

Table 4:

Highest Degree Earned

97

Table 5:

Immigrant Generation Frequencies

97

Table 6:

Race Frequencies

98

Table 7:

Nationality Frequencies

99

Table A8:

Acculturation and Health Beliefs Correlations

100

Table B8:


101

Table C8:


102

Table D8:


103

Table E8:


104

Table 9:

Acculturation Descriptives

105

Table A10:

Immigrant Generation and Health Beliefs Correlations

106

Table B10:


107

Table C10:


108

Table D10:


109

Table E10:


110

Table 11:

Highest degree earned, acculturation, and immigrant generation Correlations

111

Generational Status, MA, and In-built Weakness Health Belief Case Processing Summary

112

Table A12: Table B12:

Generational Status, MA, and In-built Weakness Health Belief Regression Model Fitting Information 112


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Table C12:

Generational Status, MA, and In-built Weakness Health Belief Pseudo RSquare 113

Table D12:

Generational Status, MA, and In-built Weakness Health Belief Parameter Estimates 114

Table A13:

Generational Status, MA, and Doctor’s Advice Health Belief Case Processing Summary

115

Table B13:

Generational Status, MA, and Doctor’s Advice Health Belief Regression Model Fitting Information 116

Table C13:

Generational Status, MA, and Doctor’s Advice Health Belief Pseudo RSquare 116

Table D13:

Generational Status, MA, and Doctor’s Advice Health Belief Parameter Estimates 117

Table A14:

Generational Status, MA, and Environmental Improvement Health Belief Case Processing Summary 118

Table B14:

Generational Status, MA, and Environmental Improvement Health Belief Model Fitting Information 119

Table C14:

Generational Status, MA, and Environmental Improvement Health Belief Pseudo R-Square 119

Table D14:

Generational Status, MA, and Environmental Improvement Health Belief Parameter Estimates 120

Table A15:

Generational Status, MA, and Quality of Surroundings Health Belief Case Processing Summary 121

Table B15:

Generational Status, MA, and Quality of Surroundings Health Belief Regression Model Fitting Information

121

Generational Status, MA, and Quality of Surroundings Health Belief Pseudo R-Square

122

Generational Status, MA, and Quality of Surroundings Health Belief Parameter Estimates

123

Table C15: Table D15: Table A16:

Generational Status, MA, and Medication Health Belief Case Processing Summary 124


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Table B16:

Generational Status, MA, and Medication Health Belief Regression Model Fitting Information 124

Table C16:

Generational Status, MA, and Medication Health Belief Pseudo R-Square 125

Table D16:

Generational Status, MA, and Medication Health Belief Parameter Estimates

126

Table A17:

Generational Status, MA, and Infection Exposure Health Belief Case Processing Summary 127

Table B17:

Generational Status, MA, and Infection Exposure Health Belief Model Fitting Information 127

Table C17:

Generational Status, MA, and Infection Exposure Health Belief Pseudo RSquare 128

Table D17:

Generational Status, MA, and Infection Exposure Health Belief Parameter Estimates 129

Table A18:

Generational Status, MA, and God Provides Health Belief Case Processing Summary

130

Table B18:

Generational Status, MA, and God Provides Health Belief Model Fitting Information 130

Table C18:

Generational Status, MA, and God Provides Health Belief Pseudo RSquare 131

Table D18:

Generational Status, MA, and God Provides Health Belief Parameter Estimates 132

Table A19:

Generational Status, MA, and Self-care Health Belief Case Processing Summary 133

Table B19:

Generational Status, MA, and Self-care Health Belief Regression Model Fitting Information 133

Table C19:

Generational Status, MA, and Self-care Health Belief Pseudo R-Square 134

Table D19:

Generational Status, MA, and Self-care Health Belief Parameter Estimates 135


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Table A20:

Generational Status, MA, and Virulence Health Belief Case Processing Summary 136

Table B20:

Generational Status, MA, and Virulence Health Belief Regression Model Fitting Information 136

Table C20:

Generational Status, MA, and Virulence Health Belief Pseudo R-Square 137

Table D20:

Generational Status, MA, and Virulence Health Belief Parameter Estimates

138

Table A21:

Generational Status, MA, and Illness as a Challenge Health Belief Case Processing Summary 139

Table B21:

Generational Status, MA, and Illness as a Challenge Health Belief Regression Model Fitting Information

139

Table C21:

Generational Status, MA, and Illness as a Challenge Health Belief Pseudo R-Square 140

Table D21:

Generational Status, MA, and Illness as a Challenge Health Belief Parameter Estimates

141

Generational Status, HA, and In-built Weakness Health Belief Case Processing Summary

142


Generational Status, HA, and In-built Weakness Health Belief Regression Model Fitting Information 142

Table C22:

Generational Status, HA, and In-build Weakness Health Belief Pseudo RSquare 143

Table D22:

Generational Status, HA, and In-built Weakness Health Belief Parameter Estimates 144

Table A23:

Generational Status, HA, and Doctor’s Advice Health Belief Case Processing Summary

145

Table B23:

Generational Status, HA, and Doctor’s Advice Health Belief Regression Model Fitting Information 145

Table C23:

Generational Status, HA, and Doctor’s Advice Health Belief Pseudo RSquare 146


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Table D23:

Generational Status, HA, and Doctor’s Advice Health Belief Parameter Estimates 147

Table A24:

Generational Status, HA, and Environmental Improvement Health Belief Case Processing Summary 148

Table B24:

Generational Status, HA, and Environmental Improvement Health Belief Regression Model Fitting Information 148

Table C24:

Generational Status, HA, and Environmental Improvement Health Belief Pseudo R-Square 149

Table D24:

Generational Status, HA, and Environmental Improvement Health Belief Parameter Estimates 150

Table A25:

Generational Status, HA, and Quality of Surroundings Health Belief Case Processing Summary 151

Table B25:

Generational Status, HA, and Quality of Surroundings Health Belief Regression Model Fitting Information

151

Generational Status, HA, and Quality of Surroundings Health Belief Pseudo R-Square

152

Generational Status, HA, and Quality of Surroundings Health Belief Parameter Estimates

153

Table C25: Table D25: Table A26:

Generational Status, HA, and Medication Health Belief Case Processing Summary 154

Table B26:

Generational Status, HA, and Medication Health Belief Regression Model Fitting Information 155

Table C26:

Generational Status, HA, and Medication Health Belief Pseudo R-Square 155

Table D26:

Generational Status, HA, and Medication Health Belief Parameter Estimates

156

Generational Status, HA, and Infection Exposure Health Belief Case Processing Summary

157


Generational Status, HA, and Infection Exposure Health Belief Regression Model Fitting Information 157


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Table C27:

Generational Status, HA, and Infection Exposure Health Belief Pseudo RSquare 158

Table D27:

Generational Status, HA, and Infection Exposure Health Belief Parameter Estimates 159

Table A28:

Generational Status, HA, and God Provides Health Belief Case Processing Summary 160

Table B28:

Generational Status, HA, and God Provides Health Belief Regression Model Fitting Information 160

Table C28:

Generational Status, HA, and God Provides Health Belief Pseudo RSquare

161

Generational Status, HA, and God Provides Health Belief Parameter Estimates

162

Table D28: Table A29:

Generational Status, HA, and Self-care Health Belief Case Processing Summary 163

Table B29:

Generational Status, HA, and Self-care Health Belief Regression Model Fitting Information 163

Table C29:

Generational Status, HA, and Self-care Health Belief Pseudo R-Square 164

Table D29:

Generational Status, HA, and Self-care Health Belief Parameter Estimates 165

Table A30:

Generational Status, HA, and Virulence Health Belief Case Processing Summary 166

Table B30:

Generational Status, HA, and Virulence Health Belief Regression Model Fitting Information 166

Table C30:

Generational Status, HA, and Virulence Health Belief Pseudo R-Square 167

Table D30:

Generational Status, HA, and Virulence Health Belief Parameter Estimates 168

Table A31:

Generational Status, HA, and Illness as a Challenge Health Belief Case Processing Summary 169

ACCULTURATION AND HEALTH BELIEFS Table B31:

Generational Status, HA, and Illness as a Challenge Health Belief Regression Model Fitting Information

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169

Table C31:

Generational Status, HA, and Illness as a Challenge Health Belief Pseudo R-Square 170

Table D31:

Generational Status, HA, and Illness as a Challenge Health Belief Parameter Estimates

171

Table A32:

Generational Status and In-built Weakness Health Belief Kruskal-Wallis Ranks 172

Table B32:

Generational Status and In-built Weakness Health Belief Kruskal-Wallis Test Statistics 172

Table A33:

Generational Status and Doctor’s Advice Health Belief Kruskal-Wallis Ranks 173

Table B33:

Generational Status and Doctor’s Advice Health Belief Kruskal-Wallis Test Statistics 173

Table A34:

Generational Status and Environmental Improvement Health Belief Kruskal-Wallis Ranks

174

Generational Status and Environmental Improvement Health Belief Kruskal-Wallis Test Statistics

174

Table B34: Table A35:

Generational Status and Surroundings Quality Health Belief KruskalWallis Ranks 175

Table B35:

Generational Status and Surroundings Quality Health Belief KruskalWallis Test Statistics 175

Table A36:

Generational Status and Medication Health Belief Kruskal-Wallis Ranks 176

Table B36:

Generational Status and Medication Health Belief Kruskal-Wallis Test Statistics 176

Table A37:

Generational Status and Infectious Organisms Health Belief KruskalWallis Ranks 177

Table B37:

Generational Status and Infectious Organisms Health Belief KruskalWallis Test Statistics 178


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Table A38:

Generational Status and God Provides Health Belief Kruskal-Wallis Ranks 179

Table B38:

Generational Status and God Provides Health Belief Kruskal-Wallis Test Statistics 179

Table A39:

Generational Status and Self-care Health Belief Kruskal-Wallis Ranks 180

Table B39:

Generational Status and Self-care Health Belief Kruskal-Wallis Test Statistics

Table A40:

180

Generational Status and Virulence Health Belief Kruskal-Wallis Ranks 181

Table B40:

Generational Status and Virulence Health Belief Kruskal-Wallis Test Statistics 181

Table A41:

Generational Status and Illness as a Challenge Health Belief KruskalWallis Ranks 182

Table B41:

Generational Status and Illness as a Challenge Health Belief KruskalWallis Test Statistics 182

Table 42:

Generational Status and Doctor’s Advice Health Belief Games-Howell Multiple Comparisons 183

Table 43:

Generational Status and Environmental Improvement Health Belief Games-Howell Multiple Comparison

184

Table 44:

Generational Status and Medication Health Belief Games-Howell Multiple Comparison 185

Table 45:

MA and HA Descriptive Statistics

186

Table 46:

Generational Status, MA, and HA Kruskal-Wallis Ranks

187

Table 47:

Generational Status, MA, and HA Kruskal-Wallis Test Statistics

188


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The Effects of Acculturation on Second-Generation Immigrants’ Health Beliefs CHAPTER ONE: INTRODUCTION According to the 2013 United States (U.S.) census data, there are 33 million second-generation immigrants in the United States (Trevelyan et al., 2016). These 36 million individuals represent 12% of the United States population. The three major groups of second-generation immigrants identified in the data are Hispanics, African Americans, and Asian Americans. While census data seems to suggest that the demographics of second-generation immigrants in Shasta County, CA, differ from that of the general United States population, they still represent a sizeable portion of the county’s residents that are not adequately addressed (Outcomes Planning and Evaluation Unit, 2014; Trevelyan et al., 2016). Shasta County Health and Human Services made addressing the health disparities and inequalities that contribute to perpetuating these discrepancies a focal point in their most recent report when discussing public health goals for the county (Outcomes Planning and Evaluation Unit, 2014). The report recognizes that the health gaps present in the county are rooted not just in systematic barriers, but also in differences in individual health behaviors. Shasta County Health and Human Services (HHS) suggested that more culturally competent health providers could potentially address some of the systematic behaviors as well as the individual behaviors more effectively (Outcomes Planning and Evaluation Unit, 2014). Therefore, it is important for counselors and mental health professionals, as well as medical professionals, to know the health beliefs of this population as agencies attempt to address the health disparities present in the community. Considering the percentage of the population that second-generation immigrants represent and the unique factors that


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both individuals and families of second-generation immigrants’ experience, it is imperative for mental health professionals and medical professionals to have as much information as possible to work adequately with this underserved population. In order for mental health and medical professionals to adequately serve this population, professionals need to account for the individual’s acculturation when providing services. Acculturation is the process of cultural and psychological change that an individual undergoes as one negotiates the customs, rules, and values of the host culture with one’s previous culture (Sam & Berry, 2010). Acculturation is important to consider because research indicates that different levels of acculturation are associated with certain health outcomes (Balcazar, Castro, & Krull, 1995). For example, high levels of acculturation are associated with low birth weight in infants, high blood pressure in men, and lower rates of childhood immunizations; however, higher acculturation is also associated with lower alcohol consumption in men, lower prevalence of obesity, and lower levels of fat consumption (Anderson, Wood, & Sherbourne, 1997; Balcazar et al., 1995; Elder et al., 1998; Peete, 1999). These varying health outcomes could be influenced by a variety of factors including, but not limited to: varying beliefs, socioeconomic status, and gender (Elder et al., 1998; Lutsey et al., 2008). This study was conducted because it was believed that the findings would provide valuable information that may be used by decision makers for various projects as well as by mental health professionals working with clients who are dealing with a physical illness. Marriage and family therapists (MFTs), for example, have been able to utilize counseling interventions for decades to help clients improve their physical health, and a better understanding of the health beliefs of various individuals could help increase the


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likelihood of developing effective treatment plans (Campbell, 2003; Sperry, 1986). Knowing the acculturation levels and health beliefs of diverse populations may be useful in developing appropriate health programs focused on improving the health of individuals in immigrant communities. The purpose of this study was to survey individuals on various aspects of health beliefs and identify possible correlations between health belief responses and acculturation levels among second-generation immigrants. The following study will detail a review of the current body of research, previous research conducted, the purpose and methodology of the current study, and the findings of this research. A discussion of these findings and the relevance to current scholarly literature, aims of the current study, the methodology of the current study, as well as a presentation of the findings and discussion of how they fit into the literature follow this. Immigration and Acculturation The term “second-generation immigrant” is typically defined as an individual being a U.S.-born child of at least one foreign-born parent (Pew Research Center, 2013; White et al., 1995). There are several life influences that may impinge on secondgeneration immigrants that many Americans may not have to negotiate, including potential language barriers and specific types of discrimination (Berry, 2005; Schwartz, Unger, Zamboanga, & Szapocznik, 2010). The current study focuses on one aspect of the second-generation immigrant experience and how it may affect other parts of an individual’s life. One unique experience that second-generation immigrants may have to negotiate is the influence acculturation may have on one’s life. Considering the numerous variables involved, such as negotiating customs, rules, and values, acculturation is not a


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process that leads individuals to the same cultural conclusion (Berry, 1990; Roche, Ghazarian, & Fernandez-Esquer, 2011; Sam & Berry, 2010). This is exemplified by Roche et al. (2011) defining acculturation as “…a complex process of adaptation to a new culture and, in practice, modifications in living and thinking” (p. 927). One’s acculturation process may be vastly different from that of other individuals depending on one’s ethnocultural background (Portes & Rumbaut, 2006; Schwartz et al., 2010). For example, Polish, Italian, and Jewish populations were considered populations that would never assimilate when they immigrated after World War I, partly because those individuals were quite different in terms of language, literacy, and economic background, as well as cultural heritage (Massey, 1995; Schwartz et al., 2010). Yet, their children looked and sounded similar to the dominant culture, so these populations assimilated quite quickly (Schwartz et al., 2010). Because the current wave of immigration stems from non-European origins, ethnicity still matters in one’s acculturation process due to the discrimination one may experience based on one’s ethnicity (Portes & Rumbaut, 2006; Schwartz et al., 2010). Schwartz et al. (2010, p. 242) argues that acculturation for immigrants of color has them “face the task of integrating themselves into a society that may never fully accept them (or their children).” Consequently, the acculturation process is not generalizable for every immigrant’s experience transitioning into American culture (Schwartz et al., 2010). How acculturation occurs in relation to the family system and the dominant culture is divided into three major theories (Ngo, 2008). These theories are: (a) unidimensional acculturation, (b) interactive acculturation, and (c) bidimensional


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acculturation (Ngo, 2008). This research will employ the bidirectional acculturation model. The first of the three theories, unidimensional acculturation, is described as assimilation of individuals in subordinate groups through adopting behaviors, values, and beliefs of the dominant culture (Ngo, 2008). One of the inherent problems with unidimensional acculturation is its inability to consider the in-group diversity that may be present (Ngo, 2008). Unidimensional acculturation was not utilized for this research because it incorrectly assumes that acculturation is a unidirectional process where an individual starts on a spectrum closer to “unacculturated” then gradually displays behaviors and acquires beliefs that approximate assimilation. Classic assimilation theory has been viewed at times as ethnocentric, with English-speaking Protestant values at the top of the hierarchy that is inevitably developed within classic American assimilation theory (Alba & Nee, 1997). The second view of acculturation is “interactive acculturation” (Ngo, 2008). Interactive acculturation is similar to bidimensional acculturation but, according to Bourhis et al. (1997), the most important addition is the aspect of relational outcomes derived from the four orientations (Berry, 1990). This is described in more detail later. There are 25 possible relational outcomes that occur through the interaction between an immigrant and the host community, resulting in some sort of combination of integration, assimilation, segregation, anomia, exclusion, and individualism (Bourhis et al., 1997). These outcomes can create an integrative experience, a problematic experience, or a conflictual experience; however, Bourhis et al. (1997) does not go into detail about what these different outcomes may look like. Ngo (2008) states that this theory shows promise,


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while also noting that there appears to be little difference between the interactive acculturation concept and bidimensional acculturation concept. Ngo (2008) purports that both bidimensional and interactive acculturation implement an oppressive viewpoint by suggesting that it is immigrants who ought to acculturate, rather than individuals in the host country. The bidimensional model of acculturation has two different dimensions. When these dimensions interact, there can be one of four general acculturative outcomes, determined by a combination of acceptance and rejection. These dimensions can reflect the individual’s outcomes related to health beliefs and acculturation or assimilation (Ngo, 2008). These four acculturative outcomes are discussed in more detail below. Researchers often use this model to predict whether an individual may experience more difficulties with either familial background or when interacting with the dominant culture during the acculturation process. Overall, this model helps explain acculturative outcomes by analyzing characteristics such as beliefs and behaviors taken on by immigrants throughout their acculturative experience. The first dimension, which was labeled “heritage acculturation” for this study, pertains to how the individual values maintaining one’s heritage, family background, and cultural background while navigating the acculturation process. If an individual values family background, then the two possible acculturative outcomes, according to the bidimensional model, are integration or separation. Conversely, if the individual did not view family background as something of value, the two possible acculturative outcomes for the individual are assimilation or marginalization.


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While the first dimension focuses on the value an individual places on one’s family background, the second dimension, which was labeled “mainstream acculturation” for this study, indicates whether or not the individual values relationships within the dominant culture of the host country (Ngo, 2008). If the individual does value these relationships, the bidimensional model states that the two options for acculturative outcomes are either integration or assimilation. However, if one does not value these relationships the bidimensional model states that the potential acculturative outcomes are separation or marginalization. As previously stated, the interactions of these two dimensions yield four different acculturative outcomes. The four outcomes that Ngo (2008) identified stem from the process of acculturation: assimilation, integration, separation, and marginalization (Berry, 1990). Berry (1990) defined assimilation as relinquishing one’s cultural identity and heritage in favor of the host culture, integration as embracing the host culture’s values without relinquishing one’s own, separation as maintaining close ties with one’s own cultural roots and refusing to adopt the host country's culture, and marginalization as being alienated because of immigrating or for one’s cultural heritage. However, bidimensional acculturation has several limitations. For example, it is conceptually limited because it fails to recognize that, within the context of intergroup relations, individuals may have a constant identity struggle that fluctuates between negotiation, creation, deconstruction, and re-creation (Dominelli, 2002). Additionally, bidimensional acculturation assumes that acculturation occurs within a context of social equality, which ignores the literature on racial and ethnic disparities across continents (Ngo, 2008).


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Acculturation is a dynamic rather than static process, as evidenced by the various potential acculturation outcomes available. While individuals adapt their behaviors, beliefs, and attitudes to changing cultural dynamics, environment also influences the acculturation process (Delvari, Sonderlund, Mellor, Mohebi, & Swinburn, 2015). These authors examined how the environment can influence an immigrant’s acculturation process in terms of their potential of developing obesity. The researchers found that acculturation pattern does not directly impact BMI or waist circumference; however, depending on an individual’s reason for migrating, the size of one’s prior town of residence as well as one’s prior socioeconomic status were identified as influential variables. This information is pertinent to the study because it shows the lack of choice that families may give children (i.e., second-generation immigrants) regarding a move, which may adversely affect one’s acculturation process within the host culture. Iqbal, Oraka, Chew, and Flanders (2014) found that acculturation and environment, as evidenced by length of residence, might influence health outcomes. Previous research found that an immigrant mother’s length of residence in the new country may influence second-generation immigrant children’s risk of developing celiac disease (Wingren, Agardh, & Merlo, 2012). While the researchers did not identify a conclusive reason for this, they pointed towards the potential influence of environmental factors due to the increased risk being identified for mothers who immigrated from all over the world. Also, Iqbal et al. (2014) found that individuals with higher levels of acculturation were more likely to have asthma than those with lower levels of acculturation. Additionally, the environment includes an individual’s social environment where one’s acculturation process is influenced by perceived discrimination targeting the


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individual’s ethnocultural background (Schwartz et al., 2010). Depending on an individual’s ethnocultural background, the interaction between one’s acculturation process and the dominant culture can be influenced greatly due to potential discrimination (Berry, 2005). The term “ethnocultural” refers to a particular ethnic group, defined by a characteristic that a group of individuals shares such as culture, language, race, or religion (Oxford University Press, n.d.). Race and ethnicity are often used interchangeably, but the terms have different meanings. Race is a social construct typically conceptualized by an individual’s phenotypic characteristics, such as skin color and hair texture, but can also include an individual’s blood type. However, race can be an aspect of an individual’s ethnicity. Ethnicity describes cultural characteristics such as religion, language, and nationality (Cartmill, 1998; Santos, Palomares, Normando, & Quintao, 2010). Ethnicity is a more complex concept. Zagefka (2009) writes about the differing concepts of ethnicity in terms of a spectrum that features two conceptual opposites. One side can view ethnicity from an “impermeable essentialist” definition in which an individual whose parents were German would always be German despite potentially being ignorant of this background and living in a different country. The opposite of this definition is a postmodern perspective that suggests that ethnicity is a social construction, as evidenced by “invented traditions” linking some ethnic communities (Zagefka, 2009, p. 230). Due to the range of definitions for ethnicity, it is important to attempt to incorporate both ends of the spectrum when including an individual’s ethnicity in research. In order to best address this topic in research, Zagefka (2009) suggests allowing individuals to self-identify in regard to one’s own ethnicity would be the best option to include both ends of the definitional spectrum.


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Another important term is “dominant culture.” Scott and Marshall (1998) define the “dominant culture” as the culture whose traits and customs prevail over the other cultures in a society through legal or political suppression, or through being in control of media communication (Scott & Marshall, 1998). It is important to understand the role that acculturation can play in one’s life because it can affect one’s beliefs. Beliefs can be passed from generation to generation through families (Burr, Kuns, Atkins, Bertram, & Sears, 2015). If beliefs are passed from generation to generation, then it is plausible that a health belief may be passed from one generation to the next (Marshall, Jones, Ramchandani, Stein, & Bass, 2007). This trend was seen in a 2013 study that found that African-American grandmothers might have great influence on their granddaughters’ health beliefs (Taylor, Peternell, & Smith, 2013). The researchers stated that it is possible that this trend was a result of the strong maternal hierarchy, and the requisite respect provided to elders, in many African-American families (Taylor et al., 2013). There are numerous ways to assess acculturation, but it is arguable that any measure of acculturation does not adequately explain the unique factors of the acculturation experience (Berry, 1990). One method of measuring acculturation is by nativity, or generational status of the individual in the country in which he or she resides (Singh, Kogan, & Stella, 2009). One could also utilize an acculturation scale to assess the demographic factors more thoroughly (Lin, Simoni, & Zemon, 2005). Other methods of assessment may include measuring how long the individual has lived in the country or one’s primary language (An, Cochran, Mays, & McCarthy, 2008). By measuring an individual’s level of acculturation with scales that assess specific types of health beliefs,


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researchers can identify information that reveals more nuanced relationships in the data. Also, using a scale allows for quantifiable and more objective measurement than other evaluations of acculturation may provide. Health Beliefs and the Health Belief Model A health belief is a personal conviction that influences one’s behaviors in relation to health (Mosby, 2009). These health beliefs do not have to influence one’s behavior in a way that results in a health-promoting action; rather, a health belief can also influence one’s executing an action that may be negative for one’s health (Amador, Flynn, & Betancourt, 2015). For example, if one believes that one’s healthcare provider lacks professional empathy and displays negative emotions, one may be more likely to discontinue care (Amador et al., 2015). Health beliefs vary among individuals, and there are many of them (e.g. following the advice of a doctor can speed up the healing process, the amount of time it takes to recover from an illness is largely based on luck, or the quality and comfort of one’s surroundings greatly influences one’s health) (Furnham, 1994). The health belief model (HBM) categorizes these beliefs while attempting to predict an individual’s health behaviors (Carpenter, 2010). The health belief model was developed by Rosenstock and other social scientists in the 1950s to better understand failures in convincing individuals to participate in tuberculosis screening programs (Carpenter, 2010; Rosenstock, 1974). More recently, the HBM has been applied to several aspects of health, such as how an individual responds to potential symptoms of a disease, treatment adherence for chronic diseases, and lifestyle behaviors (Lin et al., 2005; Rosenstock, 1974; Upthegrove, Atulomah, Brunet, & Chawla, 2012). The health beliefs that compose the HBM pertain to:


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modifying variables, perceived benefits, perceived barriers, perceived severity, perceived self-efficacy, perceived susceptibility, and cues to action (Carpenter, 2010; Rosenstock, Strecher, & Becker, 1988). Modifying variables. Modifying variables are characteristics such as race, age, socioeconomic status (SES), and gender (Rosenstock, 1974). For example, if a study utilized the HBM to determine factors affecting smoking, SES could be considered a modifying variable since low SES is associated with a higher prevalence of smoking (Reisi et al., 2014). Gender could also be a modifying variable, since women are more likely to have an annual health check, seek advice from their doctor, and attend educational classes that related to health (Deeks, Lombard, Michelmore, & Teede, 2009). Other modifying variables include the individual’s perceived susceptibility and one’s perceived severity. These combine to create an individual’s perceived threat of an illness, as well as cues to action. Cues to action are also considered a modifying variable because certain cues may hold more influence for an individual than others. Cues to action. Cues to action are events, or prompts, that trigger an individual to act on a specific health behavior (Rosenstock, 1974; Saunders, Frederick, Silverman, & Papesh, 2013). Potential cues to action include when an individual receives information about a particular ailment or health service (Austin, Ahmad, McNally, & Stewart, 2002). Certain cues may also have a greater effectiveness for triggering individuals to act on specific health behaviors. For example, a sample of acculturated Indian men found that either experiencing a heart attack or receiving information from a physician about heart conditions were the most salient cues for the belief that healthier eating habits could improve one’s health (Hendriks, Gubbels, Jansen, & Kremers, 2012).


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Perceived benefits. Perceived benefits are an individual’s assessment of how valuable it may be to act on a health-promoting action (Rosenstock, 1974; Carpenter, 2010). For example, a perceived benefit is when an individual uses a condom because one believes it will help prevent contracting HIV (Lin, Simoni, & Zemon, 2005). Another example of a perceived benefit is when an individual believes that counseling, such as cognitive-behavioral therapy, can reduce the stressor causing one’s headaches (Harris, Loveman, Clegg, Easton, & Berry, 2015). Perceived barriers. Perceived barriers are obstacles that an individual thinks may reduce the ability to act on a health-promoting behavior (Rosenstock, 1974; Jones et al., 2015). One common example of a perceived barrier is a lack of adequate insurance coverage, which can impede an individual’s ability to seek counseling or medical care (Lee-Lin et al., 2008). However, perceived barriers are not to limited to issues like cost of care; they also pertain to feelings of modesty or fear of an operation being painful (Lee, Kim, & Han, 2009; Haworth et al., 2014) Perceived severity. Perceived severity is an individual’s perception of how severe a potential ailment or illness may be (Rosenstock, 1974). If an individual believes that contracting HPV will lead to the development of cervical cancer, then the individual may perceive HPV to have potentially serious consequences (Baldwin, Bruce, & Tiro, 2012). Also, if one believes that diabetes is a serious disease, one may be more likely to take actions to improve one’s glycemic control (Polly, 1992). This information is relevant for MFTs because individuals who have well-regulated glycemic control are more likely to have higher marital satisfaction than individuals with poorly regulated glycemic control (Trief, Himes, Orendorff, & Weinstock, 2001).


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Perceived self-efficacy. Perceived self-efficacy is an individual’s belief that he/she is capable of acting on a certain health behavior (Buglar, White, & Robinson, 2010; Rosenstock, Strecher, & Becker 1988). One example of this is that an individual who feels able to make healthy decisions without support from peers is more likely to eat a healthy diet despite a lack of peer support (Fitzgerald, Heary, Kelly, Nixon, & Shevlin, 2013). Rosenstock et al. (1988) state that this component of the HBM was added in order to better explain differences in health behaviors. For example, self-efficacy is a strong predictor of an individual’s ability to successfully combat substance use disorders (Kadden & Litt, 2011). Perceived susceptibility. Perceived susceptibility is an individual’s selfassessment of how likely one is to develop an illness (Carpenter, 2010; Rosenstock, 1974). For example, Lee, Kim, and Han (2009) found that if Korean immigrants believed they were susceptible to developing breast cancer, they were more likely to seek out mammograms. Similar findings were reported for Samoan and Turkish individuals as well (Wu & Ronis, 2009). Predictive ability. The HBM has significant empirical support, thus reinforcing its validity as a model capable of making predictive attributes for health behaviors of individuals (Carpenter, 2010). However, it should be noted that a meta-analysis examining the model’s predictive ability found that only perceived benefits and perceived barriers were significantly associated with being able to predict health behaviors (Carpenter, 2010). A separate review found that perceived susceptibility, perceived benefits, perceived barriers, and perceived severity have been identified as significant predictors (Orji, Vassileva, & Mandryk, 2012). Nevertheless, the HBM is an appropriate


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framework for this research due to the study’s purpose of investigating the influence that acculturation may have on health beliefs in second-generation immigrants. Cognitive-Behavioral Therapy and Health Beliefs Cognitive-behavioral therapy is a natural therapeutic modality in which to utilize the health belief model due to their shared assumption that beliefs influence behaviors. One potential way to incorporate the health belief model into a CBT treatment is to utilize the HBM framework to assess a client’s beliefs and likelihood to follow through on certain behaviors (Gipson & King, 2013). The HBM could be used to assess suicidal clients and drug users, among others (Wagner, Unger, Bluthenthal, Andreeva, & Pentz, 2010). Interaction of Acculturation and Health Beliefs Health beliefs are influenced by acculturation, and acculturation is, in turn, impacted by one’s health beliefs (Ronancio, Ward, & Berenson, 2011; Yarova, Covan, & Fugate-Whitlock, 2013). Berry (1990) reported four different acculturative paths: a) full assimilation, embracing all of the common health beliefs that many Americans identify with; b) integrating concepts of the typical American health belief into one’s own health behavior decisions; c) separation, in which one does not relinquish one’s health beliefs; or d) the individual is ostracized within one’s own new community for retaining her/his original health beliefs. The various methods of acculturation lead to a wide array of beliefs and behaviors among immigrant populations and subsequent generations (Bourhis et al., 1997; Palmquist, Wilkinson, Sandoval, & Koehly, 2012). One of the health beliefs that is most affected by acculturation is the degree of fatalistic beliefs that an individual may hold (Roncancio, Ward, & Berenson, 2011). Rice


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(2014) defines a fatalistic belief as a belief that an outcome is outside one’s control. For example, an individual may feel that one does not need to have breast cancer screenings because God controls everything. This type of fatalistic belief is viewed as a certain interpretation of religious theology that has been found to be independently correlated with cancer incidence as well as cancer screening behaviors (Padela et al., 2015). Similarly, some believe that karma is a major influence on the outcome of situations, and that any outcome may simply be fate. It may be difficult to convince individuals to seek preventative care or engage in health-promoting behaviors because individuals may attribute health and health outcomes to the grace and judgment of God (Guilfoyle, Franco & Gorin, 2007; Lee & Vang, 2010). These beliefs remove the burden of care from the individual, allowing one to rationalize not acting on health-promoting behaviors (Gonzalez et al., 2014). When an individual is able to rationalize delaying or ignoring health-promoting behaviors, such as maintaining a healthy diet, it can lead to adverse health outcomes later in the individual’s life. Other health care beliefs pertain to topics such as diet, physical activity, lifestyle behavior, emotions, and beliefs about medical care. Several articles discuss how individuals believe a healthy diet can help promote health (Im & Chee, 2005; Liang, Yuan, Mandelblatt, & Pasick, 2004; Pham et al., 2007). In contrast, several other studies have found beliefs that an Americanized diet is unhealthy and causes disease (Chen et al., 2006; Gonzalez et al., 2014). Similarly, Zhao, Esposito, and Wang (2010) report that individuals from a variety of backgrounds believe physical activity is health promoting. Lifestyle behaviors that individuals believe may cause disease are alcohol consumption and smoking (Gonzalez et al., 2014). Other studies note a view that disease


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can arise from feeling stressed, emotionally distant, and sad (Gonzalez et al., 2014; Pavlish, Noor, & Brandt, 2010; Scanlon et al., 2006). In addition to the beliefs pertaining to lifestyle behaviors, beliefs pertaining to medical-seeking behavior were also identified in the literature (Perkins et al., 2013; Wagner, Kuoch, Tan, Scully, & Rajan, 2013). While there were articles that report Western medicine being viewed in a positive light (e.g., Perkins et al., 2013; Rothstein & Rajapaksa, 2003), others saw Western medicine as only needed in an emergency, or even as untrustworthy by those who were less acculturated (Pavlish, Noor, & Brandt, 2010; Sobralske, 2006; Wagner et al., 2013). Health care professionals who are aware of these beliefs may be better equipped to provide care to individuals from diverse ethnic backgrounds. It is paramount for mental health professionals, physical health professionals, and agencies to know how to best provide care and outreach to individuals. To deliver care appropriately and efficaciously, providers must know the health beliefs that an individual may hold (Campinha-Bacote, 2003). By understanding an individual’s health beliefs, providers can learn how to best provide care and outreach (Fayanju, Kraenzle, Drake, Oka, & Goodman, 2014; Swanson, 1993). An individual’s level of acculturation is also important for practicing MFTs (Negy, Hammons, Reig-Ferrer, & Carper, 2010). The authors found that the stress that comes from the process of acculturation is independently linked with marital distress for Mexican-American women. Sarmiento and Cardemil (2009) found that acculturative stress paired with poor family functioning was significantly related to higher scores for depression among Latinas. Acculturative stress and poor family functioning is exacerbated by the shifting power differential in immigrant families where children


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function as an intermediary, often for translating, between parents and the systems in which the family operates (e.g., educational, medical, legal, housing; Arbona et al., 2010; Orellana, Dorner, & Pulido, 2003). Some of the ways that providers can utilize the findings from this study include assessing individuals’ concerns, beliefs, or knowledge of a health topic such as treatment adherence or diet. Additionally, counselors may utilize the findings of this study to educate their clients on topics of health promotion. One of the most common ways that counselors have been able to discuss health promotion with clients is by highlighting the potential benefits that decreasing the stress in one’s life can have on both an individual’s mental health as well as physical health (Bohlmeijer, Prenger, Taal, & Cuijpers, 2010; Russell & Roter, 1993). Furthermore, these findings may be pertinent when working with families of an individual battling a chronic illness because families can hold various health beliefs that may influence family distress (Arestedt, Benzein, & Persson, 2015; Rolland, 1994; Sperry, 1986). The findings may be pertinent for counselors because possessing a concept of various health beliefs can help provide effective therapy when attempting to work on the relationships and communication patterns that may be influenced by an individual with a chronic illness (Kowal, Johnson, & Lee, 2003; Livneh & Antonak, 2005). For example, MFTs can help an individual work through the process of preparing to disclose one’s HIV status more effectively when the therapist understands the stigma that may come with the condition (Cho & Chu, 2012; Serovich, 2000). Also, health agencies, counseling agencies, and social services can develop programs that disseminate relevant information about available services in the community through avenues such as


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social media applications and community outreach programs (Kratzske, Amataya, & Vilchis, 2013; Vallejos, Strack, & Ronson, 2006). Subsequent generations are of particular interest to the current study since they represent a blend of their parents’ beliefs and the beliefs of the dominant culture. Research has found that the further a generation is removed from the original immigrant generation the more it will resemble one assimilated with the dominant culture (Pew Research Center, 2013). The Pew Research Center’s survey (2013) provides evidence that the beliefs of second-generation immigrants differ from first-generation immigrants’ beliefs; other research has supported this (Scanlon et al., 2006; Van de Vijver, 2007). However, there is no extant research identifying how acculturation affects the health beliefs of second-generation immigrants. The purpose of this study is to bridge this gap in the literature. This study utilized the Health Belief Questionnaire to measure the general health beliefs of respondents and the Vancouver Index of Acculturation to assess respondents’ level of acculturation (Furnham, 1994; Ryder, Alden, & Paulhus, 2000). These measures and a demographic questionnaire were developed into an electronic survey. This electronic survey was distributed to potential participants utilizing a listserv from Simpson University and Shasta Bible College. Additionally, the survey was posted on social media, specifically Facebook, and was disseminated to other individuals through snowball sampling. This allowed for the ability to share the survey with students, faculty, and staff at Simpson University as well as members of the larger Shasta County community.


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This current study investigated the health beliefs of second-generation immigrants, specifically how acculturation may influence one’s health beliefs. The first hypothesis was that immigrants’ generation (first, second, etc.) would be correlated with health belief scores. The second hypothesis was that mainstream acculturation scores (MA) would be correlated with immigrant generation; conversely, heritage acculturation scores (HA) would be negatively correlated with immigrant generation. The third hypothesis was that the health beliefs of individuals would differ significantly between immigrant generations, and the fourth that acculturation scores will differ significantly between immigrant generations. Lastly, the fifth hypothesis was that acculturation would influence health beliefs.


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Chapter Two: Literature Review Health beliefs are personal convictions that influence one’s health-related behaviors (Mosby, 2009). As health beliefs form one’s concept of health and illness, it is likely that individuals’ health behaviors develop from the same health beliefs (Calnan & Rutter, 1986; Carpenter, 2010). The following literature review reveals that acculturation influences health beliefs and ways in which counselors have been able to work successfully with clients’ health beliefs. Additionally, the literature indicates that health behaviors differ based on level of acculturation. It appears that individuals express certain behaviors that may be different from their parents; however, the literature does not indicate the specific health beliefs impacted by the generational divide. Moreover, the literature did not explain which health beliefs may be most influenced by acculturation. Therefore, it is likely that acculturation also affects health beliefs in second-generation immigrants. Counseling and Health Beliefs The health belief model has been utilized across several cultural groups as a way to assess an individual’s suicide risk upon entering therapy (Gipson & King, 2012). Similarly, the HBM has been employed as an assessment tool with intravenous drug users to help therapists identify the beliefs that perpetuate an individual’s drug use as well as what beliefs reinforce sobriety efforts (Wagner et al., 2010). Furthermore, utilizing the HBM as an assessment tool in conjunction with cognitive-behavioral therapy has been found to be an effective way of increasing an individual’s treatment adherence (Gipson & King, 2012).


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Rolland (1987) purports that therapists have been working with families for decades on many topics, including understanding the family’s health belief system. Rolland (1987) provides case vignettes in which family therapists worked with families and medical professionals to create a partnership that bridged a difference in health beliefs to effectively deliver the appropriate medical care. Rolland (1994) discussed how a therapist working on a couple’s differing health beliefs regarding chronic illness was paramount to decreasing the marital distress that developed when an individual was diagnosed with multiple sclerosis. Another study suggested how the stress that can arise in families due to conflicting beliefs might be reconciled through therapeutic interventions (Arestedt, Benzein, & Persson, 2015). In this study, it was noted that even subtle differences in the core beliefs pertaining to illness could affect how one’s belief system may influence one’s life. For example, individuals who held that illness was a part of life believed that it should not be thought about much and should not impede daily life. In contrast to those who viewed chronic illness as a threat to life, these individuals thought that the illness made it hard to contemplate the future and that it was unfair to be affected by the illness. When working with clients with lower back pain who held health beliefs that made them feel they could not exercise, counselors have successfully helped these clients develop beliefs that allowed the client to exercise (Buus, Jensen, Maribo, Gonge, & Angel, 2014). Sperry (2007) discussed how a counselor who was willing to explore and reframe a client’s health beliefs was able to successfully have the client utilize a psychological intervention for anxiety rather than solely a pharmacological intervention.


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In addition to the work that family therapists and therapists working with individuals have conducted regarding health beliefs, research has shown group therapy that includes a discussion of health beliefs can increase adherence to anti-cancer therapies through group members sharing accurate information pertaining to the potential benefits of cancer treatments (Kissane et al., 2004). Additionally, MFTs may incorporate families’ health beliefs as a fundamental aspect of therapy within the specialization of “medical family therapy” (Bischof, Mohr, & Lieser, 2004; Falke & D’Arrigo-Patrick, 2014). Due to their role in working with professionals who deal directly with physical illnesses, medical family therapists have a unique opportunity to help other professionals assess whether symptoms have a medical or psychological cause. For example, individuals with lower levels of acculturation were more likely to interpret symptoms of separation anxiety as having a medical cause, rather than a psychological one (Sood, Mendez, & Kendall, 2012). Due to the unique nature of this position, and the knowledge that medical family therapists have regarding psychological conditions, medical family therapists can utilize information pertaining to an individual’s health beliefs to better assist with client care. Health Belief Model The health belief model (HBM) was developed in the 1950s when public health programs were being established (Finfgeld, Wongvatunyu, Conn, Grando, & Russell, 2003). The HBM is a psychological model developed to help explain and predict an individual’s health-related behaviors (Carpenter, 2010; Janz & Becker, 1984). The model utilizes six major concepts: perceived susceptibility, perceived severity, perceived


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benefits, perceived barriers, cues to action, and perceived self-efficacy (Carpenter, 2010; Janz & Becker, 1984). Perceived susceptibility is one’s perception of how likely it is that one may become ill or develop an ailment, while perceived severity is one’s opinion of the severity of the symptoms of an illness and what the potential consequences may be. Perceived benefits are defined as one’s view of how efficacious a specific health behavior may be in addressing or preventing an illness. Tangible obstacles in the process of obtaining care (e.g., cost, access to care) are known as perceived barriers. Cues to action are motivating factors that lead an individual to display a certain health behavior, and perceived self-efficacy is one’s confidence in one’s ability to execute a health-related behavior. The HBM has been implemented successfully in the field of marriage and family therapy as well as other mental health fields. For example, Cole, Stevenson, and Rodgers (2009) used the HBM to guide their study on the influence of cultural health beliefs on self-reported mental health status and the utilization of mental health services. The HBM has also been employed to better understand the factors that influence adolescents’ mental health help-seeking behavior (O’Connor, Martin, Weeks, & Ong, 2014). Borowski and Tambling (2015) utilized the HBM to categorize the beliefs and preferences that young adults hold towards premarital counseling to better conceptualize how counselors can increase participation in premarital counseling programs. Furthermore, the HBM has been utilized to conceptualize the diverse beliefs individuals from various cultural backgrounds may hold towards suicide. A case study demonstrated how conceptualizing an individual’s beliefs with the HBM could help counselors utilize cognitive behavioral therapy to address barriers to treatment (Gipson & King, 2012).


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Additionally, the HBM has an extensive history of utilization and is among the most popular psychological frameworks guiding research and practice (Finfgeld et al., 2003). However, there are some possible drawbacks to applying the HBM. One limiting factor regarding the HBM is that it assumes that all individuals value health, which may not be accurate (Wagstaff, 2007). Moreover, the HBM has a modest predictive ability and lacks clarity regarding the relationship between the various concepts within the model (Orji, Vassileva, & Mandryk, 2012). The HBM is an appropriate framework for this study because it categorizes the health beliefs of individuals into six relevant categories. Numerous studies have demonstrated the HBM’s ability to accurately categorize health beliefs based on the model’s six core concepts across a diverse group of populations and when investigating diverse points of focus (Lee, Stange, & Ahluwalia, 2014; Lin, Simoni, & Zemon, 2005; Wu & Ronis, 2009). One such study is that of Lee et al. (2014) in which they examined the breast cancer screening behaviors of Korean women, utilizing the health belief model as the framework for their research. While their study focused primarily on breast cancer, they found the top five perceived risk factors for developing cervical cancer were poor hygiene, use of birth control, using an IUD, having several miscarriages, and having very frequent sexual activity with the same man. The Lee et al. (2014) study is one of many that utilized the HBM to assess individuals’ beliefs regarding breast cancer screening. Other examples include McGarvey, Clavet, Li, Butler, Cook, and Penino (2003); Wu & Ronis (2009); and Fulton, Rakowski, and Jones (1995). Also, Lin et al. (2005) utilized the health belief model to categorize how Taiwanese immigrants felt about HIV protection and condom use. The authors found


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that, when controlling for other demographic variables, acculturation greatly influenced perceived benefits. For example, individuals who were more acculturated had higher rates of perceiving benefits of consistent condom use and also reported more consistent condom use than individuals who were less acculturated. A similar study utilized the HBM with African immigrants (Asare & Sharma, 2012). Asare and Sharma (2012) had similar findings in regards to the effects of acculturation, as Lin et al. (2005) identified in their own study. Additionally, the health belief model has been adapted across numerous treatment modalities, including some for mental illness (Upthegrove et al., 2012). Efficacy of the health belief model is evidenced by its extensive application in studies examining behaviors relating to human papillomavirus (HPV) vaccinations and screenings. For example, Scarinci, Bandura, Hidalgo, and Cherrington (2012) utilized the HBM while developing a theoretically based intervention on cervical cancer prevention among a Latina population. Sundstrom et al. (2015) utilized the HBM as a theoretical framework for a web-based survey assessing the beliefs of college-age women in order to increase the likelihood of HPV vaccination. Guilfoyle, Franco, and Gorin (2007) also utilized the HBM as a framework to assess cervical cancer screening behaviors among elderly women. Additionally, the HBM has been employed to assess an individual’s likelihood of adhering to one’s treatment regimen (Kawakami et al., 2014). Kawakami et al. (2014) found that treatment adherence was lower for individuals with Crohn’s disease when an individual’s cues to action decreased (e.g., absence of visible bleeding, eight daily tablets or less taken). Furthermore, Begley, McLaws, Ross, and Gold (2008) looked at treatment adherence for HIV antiretrovirals and found that their modified HBM accurately


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classified the individuals by their likelihood to adhere to treatment. Furthermore, a study on low SES men and their likelihood to obtain early detection prostate exams has been applied to the HBM (Dale, Sartor, Davis, & Bennett, 1999). That study found that individuals were less likely to utilize early detection prostate exams due to the barriers involved in trying to get the screening completed (e.g., monetary cost, time, negative impressions of prostate exams). The benefits of the HBM include its effective application across different ethnic populations, different genders, and a wide variety of health contexts. For example, the HBM has been applied to males in regard to testicular cancer self-screening behaviors (Brown, Patrician, & Brosch, 2012). They found that emphasizing the perceived benefits of self-screening led to an increase in the likelihood of adopting the self-screening behavior. However, a separate study by Trankle and Haw (2010) found that perceived benefits did not increase the likelihood of new behavior development; rather, it was perceived susceptibility that increased the likelihood of new behavior change. This finding is not true for perceived susceptibility to breast cancer because of the widely known severity of the disease (Wu & Ronis, 2009). Perceived barriers were found to be the most potent predicting factor of the HBM for a sample of Chinese-American immigrants (Lee-Lin et al., 2008). Because the HBM has been applied successfully across a wide variety of contexts, the HBM can be used productively with the populations in this study. The HBM has also been utilized effectively in the Hispanic community (Shokar et al., 2015). This study accurately identified several perceived benefits of screening for colorectal cancer as well as several barriers (e.g., embarrassment, fatalistic views about


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cancer) pertaining to the screening. The HBM has now been extended to include more diverse samples as well as younger generations (Sundstrom et al., 2015). Sundstrom et al. (2015) made use of the HBM to better understand why an individual may or may not pursue an HPV vaccination. There is evidence that acculturation does have an effect on the HBM (Bereolos, 2007). This study found that level of acculturation led to various outcomes pertaining to Mexican-American’s health beliefs regarding diabetes. Acculturation and Health Beliefs As previously noted, acculturation can play a significant role in the HBM. Roche, Ghazarian, and Fernandez-Esquer (2011) explained acculturation as “a complex process of adaptation to a new culture and, in practice, modifications in living and thinking” (p. 927). There are numerous ways to assess acculturation, but it is arguable that any measure of acculturation does not adequately explain the unique factors of the acculturation experience (Berry, 1990; Lopez-Class, Castro, & Ramirez, 2011). One method of measuring acculturation is by nativity, or generational status of the individual residing in the country in which one lives (Singh, Kogan, & Stella, 2009). An acculturation scale could also assess demographic factors (Lin et al., 2005). Other methods of assessment focus on how long the individual have lived in the country or what language is spoken most often at home (An, Cochran, Mays, & McCarthy, 2008). As previously stated, Berry (1990) cites four possible acculturative outcomes for individuals: assimilation, integration, separation, and marginalization. Each of these potential outcomes involve acculturative paths that can lead to varying levels of stress as well as different types of stressors. The various acculturative outcomes of individuals can also lead to beliefs being influenced by an individual’s changing behaviors (Lee,


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Jezewski, Woo, & Carvallo, 2011). Additionally, some studies have found that individuals with lower levels of acculturation are more likely to hold beliefs that lead to a decreased likelihood of pursuing health-promoting behaviors (Edelman, Christian, & Mosca, 2009). Aside from the influence on beliefs, understanding acculturation is also important because the stress of that process may be a cause of illness in some individuals (Gonzalez et al., 2014). Culture. Culture is a concept that is composed of, but not necessarily exclusive to, a combination of language, values, beliefs, and behaviors (Roche, Ghazarian, & Fernandez-Esquer, 2011). Ethnocultural identity formation can be very strong within individuals due to the potential sense of belonging (Romero & Roberts, 1998). For example, ethnocultural influences can include an individual’s food preference (Ahlqvist & Wirfalt, 2000), which in turn can influence one’s health. Not only is it important to understand the influence that ethnocultural influences may have on an individual, but it is also vital to know that individuals and groups of individuals can have different health behaviors depending on their country of origin (Perez-Stable et al., 2001). The diversity among populations in the US has helped to develop a wide variety of health-related behaviors and beliefs. These populations are not homogenous; the literature review reveals that individuals have a variety of beliefs and that many of these beliefs contradict one another (Li, Stotts, & Froelicher, 2007; Mudd-Martin et al., 2014; Zhao, Esposito, & Wang, 2010). Before delving into how individuals view how to promote health or how illness develops, it is important to understand how various populations define health. Definitions of Health


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As previously stated, defining varying views about health is crucial to better learn how certain health beliefs are framed. Some examples include the biomedical model, the biopsychosocial model, and fatalistic beliefs. Another very practical example of how individuals define health includes viewing health as the ability to work. Brief descriptions of these frameworks are outlined below. Biomedical Model. The biomedical model of health is the standard health belief system used in the United States that conceptualizes health as the absence of disease, pain, or defect (Putsch III & Joyce, 1990; Wade & Halligan, 2004). This model focuses solely on biological factors that relate to the development of illness. Individuals across many ethnocultural groups may believe in the biomedical model of health, but less acculturated individuals who hold more fatalistic beliefs are less likely to accept the biomedical model’s perspective of health and illness (Vrinten, Wardle, & Marlow, 2016). Biopsychosocial Model. The biopsychosocial model is similar to the biomedical model, but it postulates that psychological factors (e.g., mood and personality) as well as social factors (e.g., cultural, familial, or socioeconomic) all interact with each other to varying degrees to create outcomes for health or illness (Halveka, Lucanin, & Lucanin, 2009). George Engel originally developed this approach while at the University of Rochester, and some individuals may call it the biopsychosocialspiritual model, as others have controversially included the concept of spirituality in the biopsychosocial model along with social factors (Borrell-Carrio, Suchman, & Epstein, 2004; Cohen & Koenig, 2003; Sulmassy, 2004). Furthermore, the biopsychosocial model is a critical approach for individuals who practice in the field of medical family therapy due to the various components of life that an individual works with in those settings (Bischof, 2004;


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Doherty, McDaniel, & Hepworth, 1994). Though the biopsychosocial model has been effective at providing mental health and physical health professionals with a holistic approach to treatment, the movement away from the biomedical model and towards the biopsychosocial approach has been slow according to Halveka, Lucanin, and Lucanin (2009). Fatalistic beliefs and health. In contrast to the biomedical model is the belief that events are controlled by outside forces and that humans are powerless to intervene (Niederdeppe & Levy, 2007). Throughout the literature review, fatalistic beliefs were often described as beliefs pertaining to spirits and other religiously affiliated concepts that can cause illness or promote health (Franklin, Schlundt, & Wallston, 2008). Various cultural groups subscribe to fatalistic beliefs about health. For example, Mudd-Martin et al. (2014) found that a sample of Kentuckians had fatalistic health beliefs related to their potential health-promoting behaviors. Another study states that Somali immigrant women believe that God is the main influence in promoting health (Carroll et al., 2007). Zhao, Esposito, and Wang (2010) noted that a sample of Hmong individuals believed that spirits and the loss of one’s soul are two methods of developing illness. Similarly, previous research has found a complex interaction between acculturation and religiosity (Abu-Rayya, 2007). Essentially, if an individual identifies more with one’s family of origin, or feels isolated from the dominant culture, s/he is likely to demonstrate a religiosity similar to what his/her beliefs may have been prior to immigration (Goforth, Oka, Leong, & Denis, 2014). However, those who are highly acculturated are significantly associated with low levels of spirituality and religiosity (Abu-Rayya, 2007). It has been suggested that this may be due to various acculturative


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experiences that an individual has encountered (i.e., assimilation, conflictual; Abu-Rayya, 2007). In addition, it is important to recognize that spirituality can affect health both positively and negatively (Jones et al., 2015). A recent study found that positive spirituality is associated with better mental health, but negative spirituality has been found to be linked to significantly worse bodily pain, physical health, and mental health (Jones et al., 2015). While fatalistic beliefs can affect health both positively and negatively, fatalistic beliefs also make it more difficult to get individuals to seek healthcare and especially difficult to engage in preventative or health-promoting behaviors (Guilfoyle, Franco, & Gorin, 2007; Shokar et al., 2015; Wagner et al., 2013). While these studies provide a glimpse into what some individuals from these populations may believe, these beliefs are not generalizable across all cultural population. One example is the Hispanic population. While one study found that Mexican-born women believe primarily in the biomedical model, there are some individuals who also hold fatalistic beliefs, which tend to be conceptualized as being incongruent with the biomedical model, stating that God provides health and allows illness to occur (Castro, Furth, & Karlow, 1984; Soto et al., 2011). Understanding which individuals have fatalistic beliefs is important because such beliefs can cause some people to not seek the care they may need. For example, a study investigating screening behaviors in older women found that Hispanic women tend to view a higher power as being in control of their health, so they believe there is little need to be screened and doubt that screening will benefit them (Guilfoyle, Franco, & Gorin, 2007).


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Similarly, the concept of yin and yang has a religious association, but yin and yang has a place in Chinese philosophy and science (Maciocia, 2015). Yin and yang are complementary forces interconnected with one another that provide balance to each other (Oshawa, 1973). Considering that one’s health may be understood as a balanced state between illness and health, it makes sense that yin and yang could be involved in one’s concept of the maintenance of health or development of illness (Li, Stotts, & Froelicher, 2007). There are a few studies suggesting that certain populations do believe that yin and yang contributes to their state of health. Li et al. (2007) found in their study that Chinese immigrants believe health is due to a balance of yin and yang. Additionally, in the few studies conducted with a Hmong population, Lee and Vang (2010) found that Hmong individuals thought that an imbalance of yin and yang might contribute to illness. Other definitions of health. The literature review included two studies regarding how immigrant populations define health in diverse and non-dominant ways. For example, Hmong individuals tend to perceive health as the absence of illness, the presence of psychological and emotional stability, and harmony with one’s family (Pham, Harrison, & Kagawa-Singer, 2007). In Sobralske’s (2006) study, Mexican-American men viewed illness as an absence of health, with health defined as one’s ability to work and provide for one’s family. Etiology of disease. Along with the ways in which individuals may view health, there is also the concept of how individuals believe disease develops. For example, one study found that Chinese-American women believe that one of the factors that cause cancer is built-up anger (Gonzalez et al., 2014). Furthermore, Gonzalez et al. (2014) also


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found that Latinas believe stress can be a causal factor in developing breast cancer. Another example is Scanlon et al.’s (2006) study where the authors learned that Irish individuals believed that external factors and stress led to the development of cancer. Other common beliefs about developing illness include unhealthy habits and “immoral behavior.” Gonzalez et al. (2014) found that Chinese-American women believe that immoral behavior can cause cancer. Gonzalez et al. (2014) found that Latinas believe that tobacco use, drinking alcohol, and consuming caffeine can lead to the development of breast cancer. In an article addressing the stigmatizing attitudes within Korean culture towards HIV, Cho and Chu (2012) state that immoral behaviors are often perceived as the reason for contracting HIV. Additionally, the literature revealed several other beliefs about how illness develops; however, these beliefs might not be shared as widely as those previously mentioned. One study found that Somali women believed that sadness and social disconnection are factors in developing illness (Pavlish, Noor, & Brandt, 2010). ChineseAmerican women tend to believe that falling ill is simply fate (Gonzalez et al., 2014). Another study revealed that Chinese-born women might consider a lack of rest to contribute to developing hepatitis (Chen et al., 2006). In a disease-specific study, Lee, Lee, and Diwan (2009) found that Korean immigrants viewed Alzheimer’s disease as both a form of insanity and a disease that develops as a natural process of aging. One study noted that Latinas believe that breast trauma, birth control pills, or insufficient breast-feeding can cause breast cancer (Gonzalez et al., 2014). While the literature shows a variety of beliefs regarding the etiology of disease, it is important to remember that the findings identified beliefs or outcomes traditionally


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identified for various cultural groups that may not be representative of the entire population. There are various reasons why this may be the case, but Susan Lin (2012) highlights acculturation as a factor that can greatly influence beliefs and outcomes. For example, Lin (2012) states that Chinese-American beliefs towards severe mental illness have traditionally been represented as being caused by supernatural forces, with symptoms having somatic roots that could be treated by traditional Chinese medicine. However, in Lin’s (2012) study that utilized a population of highly acculturated individuals, Lin found that the sample never mentioned a supernatural cause for severe mental illness. The individuals in Lin’s study (2012) stated that their symptoms were caused by structural and functional problems in their brain, and this sample also suggested recreational drug use as a potential cause for their illness more so than the literature has typically identified. Understanding how individuals perceive health and the etiology of disease is a first step towards learning an individual’s specific health beliefs. When considering the health beliefs of an individual, it is also important to know his or her level of acculturation. For example, Lee, Lee, and Diwan (2009) found that Korean-American immigrants with a low level of acculturation were less knowledgeable about Alzheimer’s disease and viewed it as a natural part of aging. Byrd, Peterson, Chavez, and Heckert (2004) showed that highly acculturated Mexican-American women were more likely to engage in cancer screening behaviors, while Mexican-American women who had low levels of acculturation did not see routine cancer screening as beneficial and tended to disregard the grave effects of cancer. However, the study found that these individuals were more likely to undergo cancer


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screening if they personally knew an individual diagnosed with cancer. Prior research has reported that individuals with low levels of acculturation were more likely to subscribe to fatalistic beliefs such as God being in control of health outcomes (Castro, Furth, & Karlow, 1984; De Los Monteros & Gallo, 2011). The authors stated that these beliefs led the individuals to see themselves as having a lower sense of responsibility or control over their own health. This decreased sense of responsibility or control over one’s own health has been found in other studies for similar reasons, including not only whether an individual may accept having a diagnosis of depression, but also whether the individual believes that anything can be done to address the depression (Meer & Mir, 2014). Lim, Baik, and Ashing-Giwa (2012) found that a sample of Asian-American breast cancer survivors with lower levels of acculturation placed their faith in the etiology and prevention of disease on external factors such as stress-management techniques, interactions with their support group, and consistently following the advice of their doctor. Individuals who were highly acculturated often made their own decisions, which reportedly included not adhering to their physician’s advice as consistently as other participants. Lim et al. (2015) also found that individuals who relied on support systems engaged in healthier behaviors, such as higher levels of exercise. Rodriguez-Reimann, Nicassio, Reimann, Gallegos, and Olmedo (2004) reported that in their study, featuring a sample of Mexican-Americans, acculturation significantly influenced the findings. Specifically, less acculturated individuals were more likely to perceive tuberculosis as serious and demonstrated greater susceptibility than individuals who were highly acculturated. Additionally, less acculturated individuals are more likely


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to hold health beliefs that may impede health prevention, be more susceptible to disease, and believe in an external locus of control compared to individuals who are highly acculturated (Edelman, Christian, & Mosca, 2009). Many of the other beliefs that individuals possess are categorized below based on how the beliefs fit within the HBM. Perceived susceptibility. As previously described, perceived susceptibility is an individual’s perception of how likely one is to develop an illness (Carpenter, 2010; Rosenstock, 1974). If an individual believes that he/she is susceptible to contracting the disease, he or she may be more likely to seek preventative care (Rosenstock, 1974). For example, Lee, Kim, & Han (2009) reported that Korean immigrants who believed that they were susceptible to developing breast cancer were more likely to get a mammogram. Similar findings were reported in a study of Chinese-American immigrants’ beliefs regarding their susceptibility to develop breast cancer (Lee-Lin et al., 2007). Garces-Palacio and Scarinci (2012) reported that Latina immigrants were more likely to believe that they were susceptible to contracting cervical cancer if they felt that they had been exposed to HPV. Despite the number of studies reporting an association between health-promoting behavior and perceived susceptibility, several articles did not find a statistically significant association. Neither Ho et al. (2005) nor Yu and Wu (2005) found an association between perceived susceptibility and breast cancer screening behaviors. Additionally, Lee-Lin et al. (2008) found that a sample of Chinese-American immigrants did not believe they were vulnerable to developing cancer. Similar findings for another sample of Chinese-American immigrants were reported in Lee-Lin et al. (2007). Perceived severity. Perceived severity is an individual’s perception of how severe a potential ailment or illness may be (Jones et al., 2015; Rosenstock, 1974). If an


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individual believes that an illness may cause severe harm, one is likely to engage in a health behavior that may prevent the illness from occurring. For example, one study found that Asian-American college students who believed that HIV/AIDS was a serious disease were more likely to exercise caution when choosing sexual partners, reduce their number of sexual partners, and increase condom use (Lin, Simoni, & Zemon, 2005). Additionally, Polly (1992) found that Latinos who viewed diabetes as a serious disease were more likely to try to improve their glycemic control. Chen et al. (2006) noted that Latinos who believed influenza was a serious illness were more likely to be vaccinated than those who did not think it was a concern. Perceived benefits. In contrast to perceived severity, perceived benefits are an individual’s assessment of the value of engaging in a health-promoting action (Carpenter, 2010; Rosenstock, 1974). Moreover, Rosenstock (1974) states that if an individual believes that a particular health behavior may be beneficial in promoting health, the individual is more likely to act on the behavior. For example, there are numerous healthpromoting beliefs centered on physical activity and the role of a healthy diet. Chineseborn women were found to believe that a proper diet, specifically one containing many fruits and vegetables, would prevent symptoms of certain ailments such as menopause (Im & Chee, 2005; Liang et al., 2004). Liang et al. (2004) also note that a proper diet should include food that is hot or cold in nature to balance the body’s hot/cold balance. In regards to health promotion, Korean-born women view exercise as part of the way one maintains good health (Zhao, Esposito, & Wang, 2010). Hmong women also believed that in order to maintain health, one should consume fresh fruits and vegetables (Pham et


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al., 2007). One study found that Vietnamese individuals see health promotion and illness prevention as influenced by physical activity and exercise (Zhao et al., 2010). In terms of vaccines, Perkins et al. (2013) found that Latinos may be more likely to support school-mandated HPV vaccines for boys because vaccinations may help protect both their sons as well as their potential partners. Rothstein and Rajapaksa (2003) reported that more than 80% of Indian and Chinese college students believed that rest and sleep were important to promoting health. Perkins et al. (2013) found that Latino parents believed that the HPV vaccine had enough benefits that if a doctor recommended that their son receive the vaccine, they would have him do so. As there can be an acculturation difference between the individuals in a family, various conflicts can arise that therapists and other professionals may encounter when working with second-generation immigrants. For example, Trompeta et al. (2012) found that Asian-American adolescents were more likely to have a positive attitude and perceive various benefits for both donating organs and receiving organs. Trompeta et al. (2012) reported that these positive attitudes and perceived benefits sometimes led to conflict within the adolescents’ family due to some families holding traditional values pertaining to ‘interdependence.’ Interdependence was believed to lead to this conflict because the adolescents in the study reported that they are influenced by their family’s opinion despite striving for greater autonomy (Trompleta et al., 2012). Lee-Lin et al. (2008) found that, although Chinese immigrants recognize the benefits of receiving a mammogram, few in the study sought one. However, perceived benefits were associated with mammography utilization among a Korean population (Lee, Kim, & Han, 2009). Another category of the HBM is perceived barriers.


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Perceived barriers. Perceived barriers are obstacles that an individual believes may prevent acting on a health-promoting behavior (Jones et al., 2015; Rosenstock, 1974). These perceived barriers may not be just an individual’s perception of a situation; they could be true barriers to accessing care. For example, Wang et al. (2007) states that potential barriers for Chinese women could include service providers who do not provide enough information or language services only offered in Spanish and English. Some other potential barriers include a partner’s unwillingness to practice safe sex (Jemmott, Maula, & Bush, 1999). Shokar et al. (2015) reported that the barriers in their study with low-income Hispanics were related to embarrassment and fatalistic views about cancer. Wagner et al. (2013) reported that barriers to treatment for chronic disease among Cambodian-Americans pertains to their mistrust of western medicine, cost, lack of knowledge about services, lack of transport, and language barriers. Ndukwe, Williams, and Sheppard (2013) stated in their study that fatalistic beliefs were a barrier for African immigrants as well. One study with Asian-American children suggested that cultural and linguistic barriers influenced one’s ability to act on a health-promoting behavior; consequently, Asian-American children were less likely to seek out a doctor or have a physical exam (Yu, Huang, & Singh, 2003). Also, barriers to mammography for Tamil immigrant women were cited as the procedure being painful, time consuming, and expensive (Meana, Bunston, George, Wells, & Rosser, 2001). The health beliefs one holds may have a significant influence on the behaviors one exhibits. For example, it seems that Somali immigrants place higher emphasis on caring for immediate health needs rather than long-term care or prevention due to cultural influences, lack of knowledge, and the belief that trips to the doctor without being ill are


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unnecessary (Carroll et al., 2007; Pavlish, Noor, & Brandt, 2010). Additionally, it may be difficult for clinicians to discuss cancer with some Korean-born women due to a fear that talking about cancer may bring them bad luck (Nguyen, Barg, Armstrong, Holmes, & Hornik, 2008). Similarly, Vietnamese individuals were fearful when discussing cancer because they too believe it brings bad luck (Suh, 2008). This fear may have resulted in some unintended consequences, such as Vietnamese individuals who were reported to believe that they are not vulnerable to developing breast or cervical cancer (Zhao, Esposito, and Wang, 2010). While this was proposed as a possible reason for why Vietnamese individuals have a low screening rate, other studies indicate that Vietnamese individuals do not pursue screenings due to cost of care, lack of insurance coverage, lack of knowledge pertaining to screening for cervical cancer, and feeling that the screening procedure is embarrassing (Ma et al., 2013; McGarvey et al., 2003). Cues to action. Cues to actions are events, or cues, that trigger an individual to act on a specific health behavior (Rosenstock, 1974). For example, one “cue to action” occurs when an individual receives information about the HPV vaccine from either one’s parents or physician, and the individual later decides to receive the HPV vaccine as a result of that information (Sundstrom et al., 2015). Austin et al. (2002) reports that cues to action among a Latino population may include physician recommendation, community promotions, church outreach, and media-based public health campaigns. Generally, cues to action arise from one of three domains of an individual’s life: social influence, experience, or shifts in the possibilities for change (Meillier, Lund, & Kok, 1997). Also, Gipson and King (2012) list concerns pertaining to poor job performance, discussing an


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individual’s history of depression, and suicidal ideation as possible cues to action for an individual to seek counseling. Perceived self-efficacy. Perceived self-efficacy is an individual’s belief that one is capable of acting on a certain health behavior (Carpenter, 2010; Rosenstock, Strecher, & Becker 1988). The literature revealed that many groups have differing views on how diet influences their ability to control their health. Another health-related belief indicated in the literature was the role that diet can have in maintaining health or developing illness. There are several studies that suggest this belief is common for ChineseAmerican individuals (Rothstein & Rajapaksa, 2003; Zhao, Esposito, & Wang, 2010). Other studies have found that Chinese-Americans and Chinese-born women may view an unhealthy diet as a potential cause of cancer or hepatitis (Chen et al., 2006; Gonzalez et al., 2014). Additionally, Lee, Kim, and Han (2009) as well as Gonzalez et al. (2014) found that Korean-born women and Latinas might view an unhealthy diet as a possible route to developing breast cancer or cervical cancer. Regarding health promotion, there were a few common beliefs that emerged from the literature search. For example, spiritual components play a large role in one’s view of health promotion. Somali immigrants may view God, or religion, as the main influence in their health-promoting behaviors, health habits, and avoidance of risky behaviors (Carroll et al., 2007). Castro, Furth, and Karlow (1984) found that Mexican-born women in their study believed that God provided health to them. While there is conflicting evidence regarding health behaviors and outcomes across various ethnic groups, it appears that individuals have efficacious health-seeking behaviors. Chinese-Americans attempt to gather as much information available to them


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when seeking answers to health-related questions (Chen, Kendall, & Shyu, 2010). A study investigating how best to educate the Latino community about the dangers of lead poisoning found that individuals expressed a great desire to learn what items were likely to contain lead so that they could protect their family (Vallejos, Strack, & Ronson, 2006). Additionally, college-aged Latinas expressed a desire for smartphone applications to learn about health-related information more easily (Kratzke, Amataya, & Vilchis, 2013). Because individuals generally receive information about their health from their parents and physicians, that information is most likely to inform and influence one’s health beliefs (Sundstrom et al., 2015). However, acculturation has been shown to moderate one’s health behaviors in comparison to health behaviors displayed from previous familial generations (Bereolos, 2007). One example of this is the “Latino health paradox” (Franzini, Ribble, & Keddie, 2001). As previously discussed, the paradoxical relationship between level of acculturation and expected health outcomes has shown that individuals with higher levels of acculturation have been found to be more likely to have certain negative health outcomes (Anderson, Wood, & Sherbourne, 1997; Balcazar, Castro, & Krull, 1995; Elder et al., 1998; Horevitz & Organista, 2013; Peete, 1999). There are studies that have investigated how second-generation immigrants’ health behaviors compare to parental generations, but as of this literature review there has been no study conducted to specifically investigate the health beliefs of secondgeneration immigrants. Additionally, this research is important as there is evidence that the health outcomes of those who are more acculturated may differ from those who are less acculturated. The current study is unique because it addresses the gap in the

ACCULTURATION AND HEALTH BELIEFS literature that relates to second-generation immigrants’ health beliefs and the influence that acculturation may have on their views of health.

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Chapter Three: Methodology Research Design This cross-sectional study utilized an online survey to gather data from respondents. The data was analyzed for potential correlations between immigrant generation and health beliefs, immigrant generation and acculturation, and health beliefs and levels of acculturation. The data was also examined to determine if acculturation influenced the relationship between immigrant generation and health beliefs. Furthermore, analyses were conducted to assess for statistically significant differences between immigrant generations and health beliefs. This study received final approval from the Institutional Review Board of Simpson University on February 21, 2017. Participants The participants included an array of staff, students, and alumni of Simpson University and Shasta Bible College along with members of the Shasta County community. Data pertaining to second-generation immigrants was the focus, but results pertaining to immigrant generation status were valuable as well. Therefore, there was no restriction for the participant population. However, it was difficult to obtain the necessary number of respondents needed for a representative sample of secondgeneration immigrants in Shasta County, CA. There is currently no data available regarding the population of second-generation immigrants in the county. As a result, a conservative estimate of the population will be employed, using the same population percentage of first-generation immigrants living in the county, which is 4.9% (United States Census Bureau, 2016). Based on this information, it is estimated that the adult


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second-generation immigrant population of Shasta County consists of approximately 6,885 individuals (United States Census Bureau, 2016). Furthermore, the required sample size for a representative sample of secondgeneration immigrant adults in Shasta County, CA, would be 95 participants for a 95% confidence interval and 10% margin of error. In order to achieve this, at a 20% estimated survey response rate, 475 second-generation immigrants needed to be invited to participate. Combining the 4.9% portion of the population estimated to be secondgeneration immigrants and the required number of individuals who need to be invited to participate in the survey, the final total reaches 9,500 individuals. An individual from Simpson University and Shasta Bible College made initial contact with potential participants, as the survey was sent through both Simpson University’s and Shasta Bible College’s listserv. For surveys shared on Facebook, the principal investigator made the initial contact by sharing the survey on his social media webpage. The only known existing relationship with individuals from Simpson University and the principal investigator is that the principal investigator is connected with five individuals on social media. Beyond those connections there is no known relationship that exists between individuals from Simpson University and the principal investigator because the principal investigator has only attended the university and lived in the region for graduate school. For surveys shared on social media, respondents may be more likely to answer the survey if they feel that they will be helping a friend by doing so. A major concern with utilizing social media as a recruitment tool is that the sample may be more homogenous than a sample that was recruited through other methods, which could also be exacerbated by individuals who answer the survey to help a friend. A


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separate concern when utilizing self-report data is the potential for inaccurate information, whether it is intentional or unintentional on the part of the respondent (Paulhus & Vazire, 2007). However, Ramo and Prochaska (2012) assert that the costeffectiveness of utilizing social media outweighs the concerns of homogeneity. Martinez et al. (2014) also state that utilizing social media as a recruitment tool could be helpful when trying to study hard-to-reach populations. Additionally, snowball sampling was utilized to further ensure that enough second-generation immigrants are reached in the community. Snowball sampling included the principal researcher reaching out to friends and colleagues in the local area regarding the study. Many of the friends and colleagues that were approached either shared the survey on social media or sent the survey to individuals who they thought might be interested in participating in the survey. Due to the small percentage of second-generation immigrants assumed to be in the Shasta County community, snowball sampling was seen as an effective way to reach out to the individuals (Sadler, Lee, Lim & Fullerton, 2010). Furthermore, Sadler et al. (2010) also stated that snowball sampling is a culturally competent way of recruiting individuals because the participants are contacted by people they already know. While there are benefits to this method, there are several disadvantages as well, such as the sample not being a true representation of the population, (Sadler et al., 2010). Additionally, some disadvantages could include having no reliable way of measuring if the saturation of the sample has been reached and potentially disclosing personal information to others (Sadler et al., 2010). Procedures


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An online survey was developed using Survey Monkey. The online survey included the Health Belief Questionnaire and Vancouver Index of Acculturation (Furnham, 1994; Ryder, Alden, & Paulhus, 2000). All measures used in this study utilize Likert scales with the same points as the original scale. As part of this questionnaire, participants electronically signed an informed consent form. A demographic questionnaire was developed by the principal investigator to gather information pertaining to participants’ age, sex, generational status, nationality, race, and level of education. Generational status was divided into three categories: first-generation immigrant, second-generation immigrant, and third-generation immigrant or beyond. A firstgeneration immigrant is defined as one not born in the host country, in this case the United States, to parents who also were not born in the host country. A secondgeneration immigrant is defined as an individual born in the US to a family where at least one of the parents is a first-generation immigrant. An individual born into a family where both parents were also born in the US is considered a third-generation immigrant or beyond. While an online survey generally elicits some appropriate concerns related to sampling and access issues, there are several benefits to this survey method (Wright, 2005). Wright (2005) states that some of the benefits include cost effectiveness, time efficiency, and access to unique populations that otherwise might not be included. Wright also notes that there is a small debate as to whether or not the response rate for online surveys is less than that of a traditional mail survey. However, there is evidence that mail surveys and online surveys do not differ in their response rate (Mehta &


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Suvadas, 1995; Stanton, 1998; Thompson, Surface, Martin, & Sanders, 2003). Considering the benefits of utilizing an online survey, it seems that they outweigh the concerns regarding sampling issues. The online survey was sent via a listserv to students and staff at Simpson University. The survey consisted of 37 questions, including seven demographic questions. In order to incentivize participants to participate in the survey, at the end of the survey participants had the opportunity to copy and paste a web link into a separate tab, thereby directing them to a separate survey where they were invited to enter their email address. If participants entered their email address, they were eligible to be selected for one of five $10 gift cards to be decided on at point of contact. Measures Vancouver Index of Acculturation. Acculturation was assessed using the Vancouver Index of Acculturation (Ryder, Alden, & Paulhus, 2000). Answers were recorded utilizing a nine-point Likert scale, ranging from strongly disagree to strongly agree. The Vancouver Index of Acculturation investigates acculturation by assessing an individual’s stances on values, social relationships, and adherence to traditions of bidimensional acculturation (Ryder et al., 2000). Some strengths of this measure are that is has good psychometric properties, covers multiple domains, is frequently utilized, and can assess acculturation across several ethnocultural groups (Ryder et al., 2000). In order to develop the Vancouver Index of Acculturation, the researchers conducted three studies that assessed the scale’s psychometric factors and robustness in the context of bidimensional acculturation (Ryder et al., 2000). The Vancouver Index of Acculturation consists of two subscales: heritage identity and mainstream identity.


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Heritage identity assumes an individual comes from a different culture, while mainstream identity refers to how one feels they fit into the mainstream culture. In terms of reliability, coefficient alpha scores for the heritage dimension were greater than 0.90, and the high mean inter-item correlations were greater than 0.50 for the heritage subscale. The subscale measuring mainstream identity had a coefficient alpha score greater than 0.80 and inter-item correlations greater than 0.35 (Ryder et al., 2000). As for validity, both subscales for heritage and mainstream acculturation were found to have concurrent validity (Ryder et al., 2000). Concurrent validity was evaluated with variables such as percentage of time lived in the West, percentage of time educated in the West, generational status, anticipation of remaining in the West, English as a first language, and Western identification. The mainstream subscale revealed positive correlations with the previously identified variables and correlations ranging from r = 0.26 to r = 0.57. In contrast, the heritage acculturation subscale revealed negative correlations with the variables previously listed, with correlations ranging from r = -0.17 to r = -0.39. Furthermore, averaging the total means of the heritage subscore, as well as the mainstream subscore, provides the total score for the Vancouver Index of Acculturation. The heritage subscale is defined as the odd-numbered questions on the measure in accordance to how the measure was originally developed. Therefore, the mainstream subscale is composed of the even-numbered items found on the measure. However, one modification has been made to the measure for this study. Ryder et al. (2000) wrote that the term “North American” could be replaced with “American” if the study is being conducted in the United States, so that liberty has been taken for this study.


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Health Belief Questionnaire. The health belief measure is a modified version of the Health Belief Questionnaire. The principal researcher revised the original questionnaire to reduce the number of fewer questions and potentially increase the likelihood of survey completion (Furnham, 1994b). Specifically, this study utilized questions from the second of the two questionnaires featured in Furnham’s study because the first option focuses more directly on assessing the health beliefs of Britons (Furnham, 1994). Answers were recorded utilizing a seven-point Likert scale ranging from strongly disagree to strongly agree. This measure is based on answers obtained from in-depth interviews conducted by Stainton Rogers (Rogers, 1991). The original study did not report any data pertaining to reliability or validity of the scales (Rogers, 1991). Furnham (1994) also did not test the reliability or validity of this scale, nor did another study that utilized this measure (Swami et al., 2009). Due to the lack of data pertaining to the validity and reliability of this measure, it was decided after consulting with the thesis committee supervisor that it would be acceptable to modify the measure and utilize it before assessing for validity and reliability because the original measure does not contain that data either. Swami et al. (2009) states that there is a paucity of measures assessing general health beliefs and those that are available have never been assessed for psychometric data. Other works that include this limitation include Moss-Morris et al. (2002) and Weinman, Petrie, Moss-Morris, and Horne (1996). The Health Belief Questionnaire has been utilized to assess a sample of Britons as well as a sample of Malaysian individuals (Furnham, 1994; Swami et al., 2009). Furnham (1994) categorized the responses into four categories: external treatment (e.g., items 2, 6, 8), internal treatment (e.g., items 31,


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39), external factors (e.g., items 4, 12, 19), and internal control (e.g., items 1, 21). The items highlighted previously were utilized in the modified questionnaire. Furnham (1994) stated that external factors can be characteristics such as change or products of social intervention, and external treatment looks at a respondent’s beliefs towards the effectiveness of alternative and orthodox health. Additionally, Furnham described internal treatment as a respondent’s beliefs about how much mental and behavioral factors can influence health in an individual, while inner control focuses on the idea that having the appropriate mindset and behavior affects an individual’s health. While this measure has not been validated, the findings are briefly discussed in several other articles on the health beliefs of individuals (Kluger & Tikochinsky, 2001; MacIntyre, McKay, & Ellaway, 2006; Shiloh, Rashuk-Rosenthal, & Benyamini, 2002). Statistical Analyses A cross-sectional analysis was conducted. Descriptive statistics of the sample were obtained by utilizing IBM SPSS version 22.0 to analyze the survey data. A bivariate Spearman’s rho test was utilized to assess for correlations that may be present between health beliefs and generational status as well as determining if acculturation levels influence any of these correlations. IBM SPSS version 22.0 was used. The Spearman rho is a statistical analysis that investigates the relationship between two variables. This test specifically analyzes ranked data. This statistical analysis assumes that the data is ordinal and part of a monotonic relationship, which means that the values of one variable increase as the values of the other increase, or the values of one variable decrease as the values of the other increase.


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The Kruskal-Wallis H test was also utilized to examine the difference between the means for health beliefs between immigrant generations. This analysis is an appropriate test for this data because it is a non-parametric test that can be used for ordinal data similar to that available from these surveys. With the data being ordinal, one of the four assumptions for the analysis is already met. Two of the remaining assumptions are met because the immigrant generations being utilized as the independent variable are a type of categorical data, and the health beliefs being analyzed did not have a relationship with one another. Meeting these assumptions permitted the utilization of a Kruskal-Wallis test, which left the only remaining factor to be whether the difference between the medians could be analyzed. Due to the variation in health belief responses, the shape of the distributions were not similar enough to justify analyzing the medians, which meant that only the means for the responses could be evaluated. Furthermore, because the Kruskal-Wallis analysis only determines the existence of a statistically significant interaction between the variables, a post-hoc analysis was performed to identify at what levels there may be a statistically significant difference. This resulted in utilizing a Games-Howell post-hoc analysis. The Games-Howell posthoc analysis was appropriate in this case because it does not assume equal variances, nor does it assume equal sample sizes. Both of these are important to consider because the lack of equal variances already affected the type of Kruskal-Wallis analysis that could be conducted. Also, the number of respondents in each immigrant-generation group was not equal, which means a test that does not assume equal samples sizes was needed. Similar to both the Spearman rho correlation and the Kruskal-Wallis analysis, the Games-Howell post-hoc analysis also performs its test on ranked variables.


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In addition to the analyses above, an ordinal regression was performed to explore any influence that mainstream acculturation scores or heritage acculturation scores may have on the health belief scores grouped by immigrant generation. An ordinal regression was utilized because the survey data collected is ordinal in nature. The regression analyses were performed on the relationships identified as statistically significant by the Spearman rho analysis. There were also 20 respondents that did not answer at least one question, so “hotdeck” imputation was utilized to fill in those answers while also minimizing the loss of statistical power. Hot-deck imputation is done by replacing missing values with the observed value from a respondent who has similar responses to other items (Andridge & Litte, 2010). This study utilized a random hot deck method in which the donor respondent was selected randomly from a pool of potential donor respondents. The pool of potential donor respondents was developed for each missing respondent in a two-step process. The first step was to assess similar demographic variables for the respondents with a missing item response. The demographic variables utilized for respondent similarity were age group, educational attainment, and immigrant generation. After respondents who were most similar to the respondent in comparison to demographic variables had been identified, the pool of donor respondents was narrowed based on similar answers for the missing item in the same construct. For example, if an individual did not answer a question within the Heritage Acculturation construct, answers to other items within that construct would be examined for similarities between the respondent and donor pool respondents.


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Chapter Four: Results There were three main hypotheses for this study. The first was that health beliefs would differ significantly between immigrant generations. The second hypothesis was that acculturation scores would differ significantly between immigrant generations, and the third that acculturation scores will differ significantly between immigrant generations. The fourth hypothesis was that immigrants’ generation (first, second, etc.) would be correlated with health belief scores, and the final hypothesis was that acculturation scores would significantly influence health beliefs amongst second-generation immigrant respondents. The hypotheses for this study are that those who are more acculturated will have health beliefs that are significantly different from first-generation immigrants’ health beliefs. Moreover, those who are less acculturated will have health beliefs that are significantly different than the health beliefs found in the dominant culture. Descriptive Statistics Descriptive analyses were performed on demographic variables, including means, standard deviations, and percentages when appropriate, which are provided in Tables 1 through 7. All tables can be located following Chapter five. Ranges for the Vancouver Index of Acculturation can be found in Table 9. Descriptive analyses revealed that the data was nonnormative, or not following a normal distribution. A normal distribution depicts a bell-shaped curve when random variables are placed on a graph. Because the data does not indicate a normal distribution, a Pearson’s correlational analysis could not be performed. While statistical tests such as Pearson’s correlation are widely used, they are used to analyze normative data. Therefore, Spearman’s rho was chosen for correlational analyses because of its ability to appropriately account for


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nonnormative data. As previously stated, the Spearman rho is a statistical analysis that investigates the relationship between two variables. An alpha level of 0.05 was used for all statistical analyses. After removing survey data of respondents with no responses or responses missing major constructs, 206 valid surveys remained (N = 206). As previously mentioned, 20 respondents did not answer at least one question, so hot-deck imputation was utilized to preserve characteristics of the data distribution. Of those 206 individuals, 156 respondents were female and 50 were male. There were a wide range of ages represented in the data, but the fewest number of respondents were from the “71-or-older” range (1.9%) and the “61-70” range (5.8%). The greatest number of respondents came from the “18-25” range (43.2%) and the “31-40” range (19.9%). It is possible that the age distribution is weighted towards the younger side of the choices because of the confirmed recruitment sources. These sources included the Simpson University community, Shasta Bible College community, the principal investigator’s Facebook page, and numerous individuals through snowball sampling. Individuals who were contacted through snowball sampling were reached via individuals at mental health agencies, hospitals, National University, and additional Facebook pages, as well as through friends and family of individuals in the Shasta County area. However, it is also possible that utilizing an online survey may have had some effect on this as well. All the results of the age ranges can be found in Table one (p. 92). This sample represented in all education categories as well, with the most represented categories being equal. A total of 30.1% of respondents had received a high school diploma, and another 30.1% of individuals had received a bachelor’s degree. Considering the confirmed recruitment sources, it is not surprising that over 60% of the


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sample’s highest degrees earned were high school diplomas or bachelor’s degrees. The next largest percentage of educational attainment represented in the data was an associate’s degree (16.5%). In addition, 14.6% of the respondents reported having a master’s degree, and 6.3% of respondents reported having a doctoral degree of some kind. The remaining five individuals, who represented 2.4% of the sample, reported that they had “some schooling.” Data displaying the respondents’ answers to “Highest Degree Earned” are contained in Table 4 (p. 93). In terms of generational status, 3.9% of respondents were first-generation immigrants, 11.7% were second-generation immigrants, and 84.5% of respondents were third-generation immigrants or beyond (Table 5; p. 94). Due to this small sample size, it is clear that the results of this study cannot be generalized to the rest of the Shasta County population. Furthermore, one survey was missing the respondent’s race, but the data reveals that this sample was primarily “white, non-Hispanic” (79.6%). Data pertaining to how respondents most identified in regard to race can be found on Table 6 (p. 95). The vast majority of respondents (88.3%) reported their nationality as “American.” There were several other nationalities identified by respondents: 2.4% of respondents identified as Mexican, and another 1% identified as Mexican-American. Of the sample, 2.5% reported European nationalities, with “Italian” being the most frequently identified at 1% of the total sample (n = 2). The other European nationalities endorsed were Dutch (n = 1), Ukrainian (n = 1), and Austrian (n = 1), representing 0.5% each. The majority of the Asian nationalities (5% overall) were from Eastern Asia, with 1% of the sample reporting a Chinese nationality (n = 2), 1% reporting a Hmong nationality (n = 2), 1% reporting a Filipino nationality (n = 2), 0.5% reporting a Japanese


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nationality (n = 1), and 0.5% reporting a Korean nationality (n = 1). The remaining Asian nationalities represented 0.5% of the sample each, with one individual (n = 1) identifying as Armenian and another individual (n = 1) reporting as Persian. The remaining 1% of the sample was divided equally between an Irish-American individual (n = 1) and another individual who reported as Native Hawaiian (n = 1). All relevant data can be found in Table 7 (p. 96). Mainstream acculturation scores indicated that this sample was quite acculturated, as the mean was 6.669 on a scale with a high score of nine (M = 6.669). The mainstream acculturation scores ranged from 1.1 all the way to the maximum of nine. The heritage acculturation scores revealed that this population had fairly high scores, but not quite as high as those on the mainstream acculturation scale (M = 5.981). The range for heritage acculturation was identical to the range of mainstream acculturation. Other relevant statistics are listed in Table 9 (p. 102). Correlational Analysis: Immigrant Generation and Health Beliefs A bivariate Spearman-rho correlation was performed to investigate the potential relationship between immigrant generation and health belief items, as well as highest degree earned and health belief items. This correlational analysis was performed to try and answer the first hypothesis for this study. There were no statistically significant correlations between highest degree earned and health belief items. However, there were two positive weak correlations between immigrant generations and health belief items. Immigrant generation was positively correlated with the item “I usually expect to take medicine to help me recover from my illness” (p = 0.003, rs = 0.205). The second correlation identified with immigrant generation was “When I’m ill enough to consult a


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doctor, my recovery will be faster if I comply properly with the advice and treatment I get” (p = 0.047, rs = 0.139). This means that individuals who are farther away from the first-generation were more likely to agree with the previously identified health belief items. Furnham categorized both of these health belief items under “external treatment” (Furnham, 1994). All other data for this analysis are depicted in Tables A10, B10, C10, D10, and E10 (pp. 103-107). Kruskal-Wallis Analysis In addition to the Spearman rho correlations that were performed, Kruskal-Wallis H analyses were also conducted to assess whether or there were any statistically significant differences in the mean rank between immigrant generation and health beliefs. This analysis was utilized to answer the third hypothesis of the study. Due to the differences in the number of individuals in each level of the immigrant generation variable, it would have been preferable to utilize the median rank instead of mean rank because the mean is more sensitive when the level contains fewer individuals. However, the Kruskal-Wallis H analysis requires the distribution of the dependent variables to be similar in order to compare medians; however, the data in this sample does not fit this assumption. The Kruskal-Wallis analyses found three statistically significant differences between immigrant generation and various health beliefs. The first of the statistically significant differences was between immigrant generations on the item “When I’m ill enough to consult a doctor, my recovery will be faster if I comply properly with the advice and treatment I get” (p = 0.038; x2 = 6.542). This finding suggests that there is at least one statistically significant mean difference between immigrant generations, and a


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post-hoc analysis will be able to illuminate where this difference is located. One of the other statistically significant findings was between immigrant generation and the health belief “To improve my health would require improvements in the environment in which I live” (p = 0.010, x2 = 9.186). The final statistically significant difference between health belief means and immigrant generation was seen for the health belief “I usually expect to take medicine to help me recover from illness” (p = 0.011, x2 = 8.946). While these were the only statistically significant interactions identified during the Kruskal-Wallis analysis, there were other interactions that were close to having a statistically significant difference in health belief means. For example, the interaction between immigrant generation and the health belief “When I’m ill, my recovery is influenced by the quality and comfort of my surroundings” was nearly statistically significant (p = 0.130; x2 = 4.075). The other interaction that was relatively close to being statistically significant was between immigrant generation and the health belief “I see illness as a challenge to be overcome— a determined attitude on my part can speed my recovery” (p = 0.191; x2 = 3.308). Post-Hoc Analysis A Games-Howell post-hoc analysis was conducted to clarify where the statistically significant differences are located between immigrant generations in regards to health beliefs. The first health belief that was investigated was “When I’m ill enough to consult a doctor, my recovery will be faster if I comply properly with the advice and treatment I get.” The post-hoc analysis revealed that the statistically significant difference here was between first-generation immigrants and the third-generation or beyond immigrants (p = 0.015). However, the difference between first and second-generation immigrants was also nearly statistically significant (p = 0.090).


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The second post-hoc analysis examined immigrant generation and the health belief “To improve my health would require improvements in the environment in which I live.” The post-hoc analysis revealed two statistically significant differences; the first of these differences was between first- and second-generation immigrants (p = 0.001). The second of these interactions was between first- and third-generation immigrants (p < 0.001). The final post-hoc analysis conducted was between immigrant generation and the health belief “I usually expect to take medicine to help me recover from illness.” The post-hoc analysis did not reveal any statistically significant differences. However, there were two interactions that came close to significance. The first of these interactions was between first- and third-generation immigrants (p = 0.066), and the second was between second- and third–generation immigrants (p = 0.137). Correlational Analysis: Acculturation Scores and Health Beliefs A bivariate Spearman-rho correlation was also utilized to assess any potential relationship between acculturation scores for “Mainstream Acculturation” (MA) and “Heritage Acculturation,” (HA) as well as the 10 items from the health beliefs construct. MA is the measurement of how acculturated an individual is to the predominant cultural environment in the US according to the Vancouver Index of Acculturation. HA is the measurement of acculturation that an individual has with one’s identified heritage. The analysis revealed that there was a strong positive correlation between MA and HA (p < 0.010, rs = 0.640). This means that individuals who scored highly on MA were also more likely to score highly on HA. This correlation may exist because individuals may


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embrace both the predominant culture as well as one’s own cultural heritage or because numerous individuals listed the US as their heritage culture. There are two other correlations with MA and two items on the health beliefs construct. A statistically significant, but weak, negative correlation was identified between MA and responses for “My state of health at any time is considerably influenced by whether or not I’ve been exposed to infectious or contagious disease organisms” (p = 0.003, rs = -0.207). This means that the closer an immigrant’s generation was to the original immigrant generation, the more their agreement in this belief increased. In the context of the overall study and hypotheses, one of the hypotheses was that individuals with lower MA scores would have statistically significantly different health beliefs than individuals with higher MA scores. This specific correlation does provide some support for the hypothesis, but not within the context that was expected. The health belief identified is a belief that is typically conceptualized as part of the biomedical model, which is a model that is predominantly utilized for health in the dominant culture, but the current correlation identifies a relationship, albeit a weak one, in which individuals with lower levels of MA are more likely to hold the identified health belief. Furthermore, MA had another negative weak correlation, which was statistically significant, with “I see illness as a challenge to be overcome—a determined attitude on my part can speed my recovery” (p = 0.014, rs = -0.170). These results mean that individuals who scored higher on MA were more likely to disagree with the previously identified items, which is similar to the aforementioned correlation between MA and the health belief pertaining to perceived susceptibility. The first health item falls into the “external factor” on Furnham’s Health Belief Questionnaire, while the second item was


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categorized as “internal control” (Furnham, 1994). All of these correlations can be found in Tables A8, B8, C8, D8, and E8 (pp. 97-101). Kruskal-Wallis: Immigrant Generation and Acculturation Scores The third hypothesis for this study was that acculturation scores would differ significantly between immigrant generations. In order to assess this hypothesis, a Kruskal-Wallis H test was utilized to analyze the difference of the medians. The medians could be analyzed because the distribution of the mainstream acculturation scores (Mdn = 6.900; SD = 1.555; s2 = 2.417) and heritage acculturation scores (Mdn = 6.000; SD = 1.568; s2 = 2.458) were similar in terms of their median, standard deviation, variance, skewness, and kurtosis. The skewness and kurtosis for the acculturation scores can be found in Table 45. The Kruskal-Wallis analysis revealed that the differences in the median for both MA (p = 0.341, x2 = 2.155) and HA (p = 0.620, x2 = 0.957), as grouped by immigrant generation, were not statistically significant. However, this may have been because the majority of individuals identified the US as their heritage culture. Ordinal Regression Analysis: Immigrant Generation, MA, and Health Beliefs In addition to these correlational analyses, an ordinal regression analysis was performed to assess whether or not acculturation scores influenced the health beliefs of individuals based on their immigration generation. This regression analysis attempted to answer the fifth hypothesis of this study. The first of these analyses investigated the interaction with the health belief that one has certain personal factors that influence one’s own health. The analysis revealed that the model is not a good fit (p = 0.288; x2 = 6.136; df = 6.000). Nagelkerke’s r2 showed a very weak relationship prediction and grouping (r2


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= 0.040). Any further relevant data for this analysis are available in Tables A12, B12, C12, D12, and E12 (pp. 109-111). The second ordinal regression analysis performed focused on the interaction with the health belief that when an individual is ill, one’s recovery will be faster if the individual follows the advice from one’s doctor. The regression analysis revealed that the model is a good fit; therefore, it showed that the predictors were able to reliably distinguish the responses for health beliefs (p < 0.001; x2 = 337.0; df = 6.000). In terms of the pseudo r2, Nagelkerke’s r2 supports a strong relationship between prediction and grouping (r2 = 0.888). Tables A13, B13, C13, and D13 contain all other relevant data for this analysis (pp. 112-114). Another ordinal regression analysis focused on the interaction between mainstream acculturation, immigrant generation, and the health belief that an improvement in environment would be necessary to improve one’s own health. The regression analysis found that this model was a good fit and that the predictors are able to consistently distinguish responses for health beliefs (p = 0.013; x2 = 16.19; df = 6). Nagelkerke’s r2 also revealed a very weak relationship between predictors and grouping (r2 = 0.078). Results are available in Tables A14, B14, C14, and D14 (pp. 115-117). Furthermore, another regression analysis focusing on the health belief that recovery is influenced by the quality and comfort of one’s surroundings was performed. This model was a good fit (p < 0.001; x2 = 398.0; df = 6). Nagelkerke’s r2 indicates a very strong relationship between predictors and grouping for this model (r2 = 0.912; Tables A15, B15, C15, and D15) (pp. 118-120).


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Additionally, a separate regression analysis was performed for the health belief that God has given the individual the means to improve one’s own health. The analysis revealed that the model is a good fit for predictors to distinguish the responses for the aforementioned health belief (p = 0.039; x2 = 13.283; df = 6), and Nagelkerke’s r2 shows a very weak relationship between predictors and grouping for this model (r2 = 0.067). However, this analysis revealed that one’s MA scores (p = 0.001) significantly influenced the prediction for health belief responses (see Tables A18, B18, C18, and D18 (pp. 128130). Additionally, data from the regression analyses not discussed in the previous paragraphs are located in Tables A16-D16, A17-D17, A19-D19, and A20-D20 (pp. 121126; pp. 130-135). Ordinal Regression Analysis: Immigrant Generation, HA, and Health Beliefs A separate set of regression analyses was performed to assess whether one’s heritage acculturation scores influence the health beliefs of individuals grouped by immigrant generation. This regression analysis continues to attempt to answer the fifth hypothesis for this study. One of the ordinal regression analyses did find one model that was a good fit (p = 0.016; x2 = 13.965; df = 5). Nagelkerke’s r2 evaluated the relationship between the predictors and the grouping as very weak (r2 = 0.072). All other relevant data on this regression are contained in Tables A23, B23, C23, and D23 (pp. 142-144). Furthermore, another regression analysis was conducted to evaluate the potential influence that HA had on responses for the health belief that improvements in the environment would need to occur to see an increase in one’s own health. The analysis revealed that this model is a good fit (p = 0.019; x2 = 13.550; df = 5). The Nagelkerke’s r2 indicates a very weak relationship between the predictors and the grouping for this


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model (r2 = 0.066). Tables A24, B24, C24, and D24 contain additional data (pp. 145147). In addition to the previous analyses, another ordinal regression analysis was performed focusing on the health belief that one’s recovery is influenced by the quality and comfort of one’s environment. The regression analysis revealed that this model is a good fit between the predictors and grouping (p < 0.001; x2 = 392.11; df = 5). Also, there is a very strong relationship between the predictors and grouping for this model (r2 = 0.908). The analysis found that HA was almost a statistically significant influence in this model (p = 0.098). All other relevant data can be found in Tables A25, B25, C25, and D25. Additional regression analysis data can be found in Tables A21-D21, A22-D22, A25-D25, A26-D26, A27-D27, A28-D28, A29-D29, A30-D30, and A31-D31(pp. 136141; pp. 148-168). One of the hypotheses for this study was that health beliefs of individuals will differ significantly between immigrant generations. A second hypothesis was that acculturation scores would differ significantly between immigrant generations. The third hypothesis was that mainstream acculturation scores (MA) would be correlated with immigrant generation; conversely, heritage acculturation scores (HA) would be negatively correlated with immigrant generation. The fourth hypothesis was that immigrants’ generation (first, second, etc.) would be correlated with health belief scores, and the final hypothesis was that acculturation scores would influence health beliefs. In general, while there were isolated relationships and interactions that were consistent with the alternative hypotheses, the data and analyses did not provide compelling evidence on which to reject any of the null hypotheses.


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Chapter Five: Discussion This study investigated the relationship between immigrant generation (i.e., firstgeneration immigrant, second generation immigrant, third-generation immigrant or beyond) and health beliefs. This study also examined if acculturation affected these beliefs. The first hypothesis was that immigrant generation would be correlated with health belief scores. The second was that mainstream acculturation scores (MA) would be correlated with immigrant generation; conversely, heritage acculturation scores (HA) would be negatively correlated with immigrant generation. The hypotheses for this study proposed that the health beliefs of individuals would differ significantly based on immigrant generation. Similarly, another hypothesis was that acculturation scores would be significantly different based on immigrant generation. The final hypothesis was that acculturation scores would influence the health beliefs of individuals. While there were some statistically significant findings, those results were a minority of the findings. Therefore, it would be irresponsible to do anything except accept the null hypothesis. Failing to reject the null hypothesis means that the findings from this study do not provide enough evidence to confidently state that the hypotheses were true. Correlational Analyses and Regression Analyses The results of this study revealed that the null hypotheses were not rejected because there was not enough statistically significant data to appropriately justify rejecting the null hypothesis. The null hypothesis is essentially the hypothesis in which there are no statistically significant correlations or statistically significant differences present based on the analyses of the responses provided by individuals. There were several statistically significant relationships identified, including two relationships


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between immigrant generation and health beliefs that were correlated. One correlation indicated that respondents further removed from their first-generation immigrant ancestors were more likely to believe that their recovery would be faster if they heeded their doctor’s advice and followed treatment recommendations. The other relationship suggested that the farther individuals were removed from a first-generation immigrant ancestor, the more likely they were to believe that medication helps an individual recover from illness. However, both relationships demonstrated weak correlations. There were two correlational relationships identified between acculturation and health beliefs. Both of these pertained to an individual’s mainstream acculturation score. The first of these correlations revealed a negative, albeit weak, correlation with an individual’s belief that one’s health is considerably influenced by whether one has been exposed to either infectious or contagious disease. Therefore, more acculturated individuals were less likely to believe that exposure to infectious or contagious disease greatly influences one’s state of health. The other relationship is also a negative weak correlation between mainstream acculturation score and believing that a determined attitude can speed one’s recovery. Interestingly, there was another correlation identified during the analysis: mainstream acculturation scores were strongly and positively correlated with heritage acculturation scores. This means that respondents who had high scores on mainstream acculturation were more likely to score highly on heritage acculturation as well. When examining this correlation with a unidimensional view of acculturation, which assumes the process of acculturation is akin to the process of assimilation, it suggests that the two


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constructs in this instrument measure the same thing. However, assuming the process of acculturation is akin to the process of assimilation would not be appropriate with the current understanding. The current understanding of acculturation is quite complex, involving various factors (e.g., host culture, heritage culture, societal context, familial context, institutional and organizational context) interacting to form an individual’s acculturation process (Ward & Geeraert, 2016). Nor would it be accurate to assume that the Vancouver Index of Acculturation’s dimensions of acculturation measure the same constructs. For many, acculturation is a process of feeling accepted by the mainstream culture while also balancing and potentially attempting to sustain acceptance from members in one’s heritage culture (Ngo, 2008). Therefore, these results may suggest that the respondents feel accepted by both the mainstream culture as well as their heritage culture. It is also possible that the data for heritage culture may have been biased as a result of several respondents identifying “American” and “United States” as their heritage culture. It is also necessary to specify that mainstream and heritage culture are not the same as MA and HA. MA and HA are constructs that Ryder et al. (2000) utilize to measure an individual’s acculturation in the context of the mainstream culture and one’s heritage culture. Similarly, when acculturation scores were analyzed to assess their influence on the health beliefs of individuals based on immigrant generation, there were instances where a statistically significant item was identified; the other variables in the regression analysis did not signal statistical significance. This resulted in the inability to reject the null hypothesis. However, there were instances where both MA and HA came relatively


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close to meeting statistical significance. If this had happened, then the third null hypothesis could have been rejected. Kruskal-Wallis and Post-Hoc Analysis While the regression analyses and correlational analyses had some difficulty identifying statistically significant variables, the Kruskal-Wallis analyses revealed a few statistically significant variables. However, those findings pertained to analyses of the relationship between health beliefs and immigrant generation. The Kruskal-Wallis analysis that was performed for the rank median of acculturation scores and immigrant generation did not reveal any significant findings. It is possible that there was a lack of significant findings with this analysis because the majority of respondents identified the United States as heritage culture. If more respondents had self-identified their heritage culture as something else (English, Mexican, Thai, etc.), then it is possible there would have been a statistically significant difference in the median ranks of MA and HA. As a result of the lack of significant findings, a post-hoc analysis did not need to be conducted to determine the areas of potential difference. Two statistical tests were able to reveal that there was a difference in health beliefs between individuals based on the immigrant-generation of the respondent. The Kruskal-Wallis provided clues to where the statistical differences in health belief mean scores might lie. The Games-Howell post-hoc was able to delineate exactly where the statistically significant differences in scores resided. The data revealed that the majority of the statistically significant differences were between first-generation immigrants and third-generation or greater immigrants. Additionally, the data was able to demonstrate that second-generation immigrants’ responses were incredibly similar to those who


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identified as third-generation or beyond immigrants. The data also revealed that on two of the items that were analyzed, second-generation immigrants had either statistically significant differences or were close to doing so. However, second-generation immigrants also had a statistically significant difference in health belief scores compared to those who were third-generation or beyond regarding the health belief that medication can help hasten recovery. These varying results may be due to several factors. The first option is that it is possible that the results represent the struggle that second-generation immigrants undergo when they are navigating the process of acculturation. The second option is that the varying beliefs identified fit into separate constructs within the Health Belief Model. Findings and the Health Belief Model In terms of how the findings fit within the HBM framework, the relationship between MA and the belief that one’s health is influenced by exposure to contagious or infectious pathogens fits within the construct of perceived susceptibility. If viewed in context, the relationship suggests that individuals who score more highly on MA may not be as concerned about developing an illness as others may be. Additionally, they may believe that they will not become ill following exposure to contagious or infectious pathogens. The findings for this construct are particularly interesting because prior metaanalyses have found that the construct of “perceived susceptibility” is the second strongest predictor of an individual’s health behavior, following “perceived barriers” (Jones et al., 2015). A contemporary example within our society is the “antivaccination movement.” A recent overview of the research on the movement found that the decrease in perceived susceptibility to contagious and infectious pathogens has been one of the


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constructs that has influenced individuals’ decisions to avoid vaccinations (Dube et al., 2013). Additionally, the relationship between MA and the belief that a determined attitude can help an individual recover from illness fits within the construct of selfefficacy in the HBM. The findings indicate that individuals with higher scores on MA were less likely to believe that a determined attitude can help one recover from illness. It is also unclear how the respondents conceptualized “illness.” It is possible the individuals viewed “illness” as ailments as varied as the common cold, the flu, cancer, or diabetes. While it is unknown how individuals conceptualized “illness,” the findings from this study revealed, in contrast to other studies, that individuals with higher scores of MA were more likely to hold a belief pertaining to a “determined attitude.” A determined attitude has been found to be effective at increasing positive health outcomes in both physical and mental health (Kahkonen et al., 2015; Taylor et al., 2014). Also, the relationship identified between immigrant generations and the belief that medication can help an individual recover from an illness falls into the perceived benefit construct of the HBM. That relationship fits into this category because the belief is that medication is beneficial to help one recover from illness. Unfortunately, meta-analyses have found that the construct of “perceived benefits” is not as powerful a predictor of health behavior as is perceived barriers and perceived susceptibility (Jones et al., 2015). As for the final relationship identified, between immigrant generation and the belief that recovery may be faster if the individual follows physician’s directions and treatment recommendations, it fits most appropriately within the construct of “perceived benefits.” This correlation falls most accurately in the perceived benefits construct


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because the belief is that there is a benefit to adhering to the doctor’s treatment recommendations. However, an argument could be made that this relationship is better categorized within the “modifying variables” construct. The relationship is essentially between a demographic factor (immigrant generation) and the likelihood that an individual will follow a doctor’s advice and treatment recommendations. This could be classified as belonging to the “modifying variables” construct because demographic factors, such as immigrant generation, are included within this construct. Generalizability of the Study Separately, any relationships identified are not generalizable to the Shasta County population because there were not enough respondents among the second-generation immigrant population. The estimate for the second-generation immigrant population required 95 total participants, but only 26 second-generation immigrants completed the survey (n = 26). Relatedly, the results of this study are not generalizable to the Shasta County population because the demographic information of the respondents is disproportionately educated compared to the general population in Shasta County (Table 4; Shasta County, 2014). Specifically, the respondents with a bachelor’s degree, master’s degree, and doctoral degree represent over 200% of the normal population distribution in Shasta County with those credentials. Additionally, the percentage of female respondents (n = 156, 75.7%) was disproportionately larger than that of the actual population of Shasta County. While participants in this sample are more educated than the general population in Shasta County, it is important to note that there were no correlations identified between degree earned and health beliefs. Nor was there any correlation found between degree


90

earned and either MA (r = -0.107, p = .126) or HA (r = -0.038, p = 0.587) scores. This is in contrast to prior research that has found a positive relationship between acculturation and education, but those findings utilized the Acculturation Attitude Scale instead of the Vancouver Index of Acculturation to measure acculturation (Baek Choi & Thomas, 2009; Chen, Varghese, Nurcombe, & Kenardy, 1999). The sample also contains a higher percentage of younger respondents than would be found in a representative sample, and this sample was also more racially diverse than the county’s demographics study indicates (Tables 2, Table 3, & Table 6; Shasta County Health and Human Services, 2014). This could have been the result of a combination of confirmed recruitment sources and the survey medium. The largest confirmed recruitment sources were universities and a college in the Shasta County region. In terms of the survey medium, online surveys have been recognized as being an effective medium to collect data from individuals from any age group (Hollier, Pettigrew, Slevin, Strickland, & Minto, 2016; Remillard, Mazor, Cutrona, Gurwitz, & Tjia, 2014). Furthermore, generalization of the findings is impossible because approximately half of the respondents did not report a county of residence. Most of those individuals reported that they lived in the United States of America, and one individual reported China. This was the result of utilizing an open-ended question. Applications for Practice While the findings in this study cannot be generalized, both physicians and mental health professionals may find some benefit in the findings. For example, the finding that there is a negative association between MA and the belief that a motivated attitude can improve an individual’s prognosis may influence how professionals approach care with a


91

client. If an individual has a higher level of MA, a counselor may need to make extra effort to increase the client’s motivation in therapy. Also, the relationships identified between immigrant generation and general health beliefs may be useful for therapists. One identified relationship pertained to the health belief that following a doctor’s recommendations for treatment can speed up the recovery process. This may be because some individuals view therapists and medical professionals as experts (Levitt, Pomerville, & Surace, 2016; Tobiano, Bucknall, Marshall, Guinane, & Chaboyer, 2015). This specific relationship may be beneficial for therapists, particularly when attempting to enlist clients’ engagement in homework and goal development. Another statistically significant relationship that may be helpful for both medical professionals and mental health professionals is that immigrant generation status correlates with the belief that medication can help an individual recover from illness. This correlation may validate what other research has revealed regarding first-generation immigrants and fatalistic beliefs (Caplan et al., 2013; Crawford, Ahmad, Beaton, & Bierman, 2015). It is unclear if this correlation is still present if a mental illness is identified. This is an important distinction when discussing this belief due to the stigma associated with medication compliance associated with mental illness (Velligan, Sajatovic, Hatch, Kramata, & Docherty, 2017). It may be important for mental health professionals to consider this when considering medication to address one’s mental illness. Overall, the findings of this study can provide data that may better inform professionals on various lines of questioning or potential approaches to care. Although


92

these findings may be beneficial for providers from various care backgrounds, it is important to remember that this data is not generalizable. However, there is the possibility that future research may address the limitations of this study while also building on this foundation. Implications for Future Research The results of this study may contribute to the literature pertaining to immigrant status, acculturation, and health beliefs. Prior to this study, there was no published research regarding the health beliefs of second-generation immigrants or how acculturation influences health beliefs. Therefore, this study may provide a first step toward bridging this gap in the literature. Further research may help to close this gap by utilizing representative samples. A representative sample would be beneficial because it is a sample based on the population demographics to which the research is attempting to generalize the findings (Polit & Beck, 2010). Also, a limitation in this study was the utilization of an open-ended question to identify county of residence. Therefore, a forced-choice question to ascertain where a respondent resides may be more practical when attempting to generalize the information to a larger population. It is worth noting that this study did not find a statistically significant relationship between educational attainment and acculturation scores, which as stated previously, contradicts prior research (Baek Choi & Thomas, 2009). It may be beneficial for future research to assess if there is a consistent relationship between acculturation and educational attainment. This may be achieved by ascertaining respondent’s acculturation level measured with several different acculturation instruments followed by an analysis.


93

This analysis could look for any potential relationship between acculturation scores and educational attainment. Utilizing several different acculturation scales would also ensure that the various dimensions of acculturation are included in the research. A separate, but important, aspect of this study pertains to the health beliefs instrument utilized for this survey: a modified version of Furnham’s Health Belief Questionnaire (Furnham, 1994b). It is not an ideal questionnaire for this research study due to its length and lack of data pertaining to its validity and reliability. Swami et al. (2009) has written about how the absence of a validated general health beliefs scale has plagued this field of research for decades. Therefore, a validated general health beliefs scale would be of great benefit for researchers in this area. More research is needed within this domain in order to ascertain more conclusive findings regarding the health beliefs of second-generation immigrants and the influence of acculturation on those beliefs. This research is needed to explain why there is a dearth of literature pertaining to health behaviors and health outcomes of second-generation immigrants (Bereolos, 2007). As research is conducted and more conclusive findings are published, mental health professionals and physical health professionals can utilize the findings to conceptualize and improve outreach programs and attempt to better meet the needs of the diverse, growing population in both Shasta County and the US. Conclusion This study serves as a first step to discovering whether second-generation immigrants hold health beliefs that are statistically significantly different from firstgeneration and third-generation immigrants. This study found that there is not a statistically significant difference pertaining to either of those relationships; however, this


94

study is burdened by several limitations. Prior research has found that various health behaviors and health outcomes differ significantly between immigrant generations. Within the framework of the HBM and a cognitive-behavioral approach, beliefs influence behaviors. Due to the differences in health behaviors and health outcomes between different immigrant generations, it may be that the beliefs underlying the behaviors are also different. While this study cannot be generalized to a larger population, agencies, mental health professionals, and physical health professionals can utilize the findings of the study when developing outreach programs or when conceptualizing a potential treatment plan. Additionally, further research could utilize a representative sample as well as take advantage of methodologies that promote forced-choice answers in order to adequately address the lack of clarity in this literature gap. In addition, it is paramount for a validated general health beliefs scale to be developed. Also, it is important for future research to continue to utilize acculturation instruments that take into account the complexities of the acculturation process.


95

Table 1 Demographic Descriptive Statistics N

Range Minimum Maximum

Statistic Statistic Statistic Your sex (please choose 206 1.00 1.00 one): Please select what age 206 6.00 1.00 group you fit into: Highest degree earned: 206 5.00 1.00 Generational Status: Of 206 2.00 1.00 the following definitions for generational status please choose which applies most to you. Of the following races 206 6.00 1.00 please select which you MOST identify? What nationality do you 206 15.00 1.00 most identify with? Valid N (listwise) 206

Mean Std. Statistic Statistic Error 2.00 1.7573 .02994

Std. Deviation Variance Statistic .42977

Statistic .185

7.00

2.7039 .12529

1.79829

3.234

6.00 3.00

3.1068 .10899 2.8058 .03380

1.56431 .48506

2.447 .235

7.00

3.1553 .08129

1.16667

1.361

16.00

1.7184 .17326

2.48672

6.184


96

Table 2 Sex frequencies Frequency Percent Valid Percent Cumulative Percent Male 50 24.3 24.3 24.3 Female 156 75.7 75.7 100.0 Total 206 100.0 100.0

Table 3 Age groups

18-25 26-30 31-40 41-50 51-60 61-70 71 or older Total

Frequency 89 13 41 16 31 12 4 206

Percent Valid Percent 43.2 43.2 6.3 6.3 19.9 19.9 7.8 7.8 15.0 15.0 5.8 5.8 1.9 1.9 100.0 100.0

Cumulative Percent 43.2 49.5 69.4 77.2 92.2 98.1 100.0


97

Table 4 Highest degree earned:

Associate's Degree Bachelor's Degree Master's Degree Doctorate High School Diploma Some Schooling Total

Cumulative Percent 16.5 46.6 61.2 67.5 97.6 100.0

Frequency 34 62 30 13 62 5 206

Percent Valid Percent 16.5 16.5 30.1 30.1 14.6 14.6 6.3 6.3 30.1 30.1 2.4 2.4 100.0 100.0

Frequency

Cumulative Percent Valid Percent Percent 3.9 3.9 3.9

Table 5 Immigrant generation frequencies

First-generation immigrant (neither you or your parents were Second-generation immigrant (at least one of your parents wa Third-generation immigrant or more (both of your parents are Total

8

24

11.7

11.7

15.5

174

84.5

84.5

100.0

206

100.0

100.0


98

Table 6 Race frequencies

Hispanic/Latino Black or African-American, non-Hispanic White, non-Hispanic American Indian or Alaskan Native, non-Hispanic Asian, non-Hispanic Native Hawaiian or other Pacific Islander, non-Hispanic Two or more races, nonHispanic Total

Frequency 17 1

Cumulative Percent Valid Percent Percent 8.3 8.3 8.3 .5 .5 8.7

164 2

79.6 1.0

79.6 1.0

88.3 89.3

11 1

5.3 .5

5.3 .5

94.7 95.1

10

4.9

4.9

100.0

206

100.0

100.0


99

Table 7 Nationality frequencies

American Mexican Chinese Dutch Mexican American Hmong Filipino Italian Ukranian Persian Irish American Austrian Japanese Korean Armenian Native Hawaiian Total

Frequency 182 5 2 1 2 2 2 2 1 1 1 1 1 1 1 1 206

Percent Valid Percent 88.3 88.3 2.4 2.4 1.0 1.0 .5 .5 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 .5 .5 .5 .5 .5 .5 .5 .5 .5 .5 .5 .5 .5 .5 .5 .5 100.0 100.0

Cumulative Percent 88.3 90.8 91.7 92.2 93.2 94.2 95.1 96.1 96.6 97.1 97.6 98.1 98.5 99.0 99.5 100.0


100

Table A8 Acculturation and health beliefs correlations When I’m ill enough to

Mainstream

Heritage

Acculturation Score Spearman's rho

I believe I may

consult a

have certain in

doctor, my

built

recovery will

weaknesses

be faster if I

which make me

comply

vulnerable to

properly with

Acculturation particular illness the advice and Score

or disorders.

treatment I get.

1.000

.640**

.030

.046

.

.000

.669

.511

206

206

206

206

.640**

1.000

.068

.101

.000

.

.334

.148

N

206

206

206

206

I believe I may

Correlation

.030

.068

1.000

.142*

have certain in

Coefficient

built weaknesses

Sig. (2-

.669

.334

.

.042

which make me

tailed)

vulnerable to

N

206

206

206

206

Correlation

.046

.101

.142*

1.000

.511

.148

.042

.

206

206

206

206

Mainstream

Correlation

Acculturation

Coefficient

Score

Sig. (2tailed) N

Heritage

Correlation

Acculturation

Coefficient

Score

Sig. (2tailed)

particular illness or disorders. When I’m ill

enough to consult a Coefficient doctor, my

Sig. (2-

recovery will be

tailed)

faster if I comply

N

properly with the advice and treatment I get. **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).


101

Table B8 Acculturation and health beliefs correlations

Spearman's Mainstream rho Acculturation Score

To improve my health When I’m ill, my would require recovery is influenced Mainstream Heritage improvements in the by the quality and Acculturation Acculturation environment in which I comfort of my Score Score live. surrounding. ** 1.000 .640 -.018 -.112

Correlation Coefficient Sig. (2tailed) N Heritage Acculturation Correlation Score Coefficient Sig. (2tailed) N To improve my health Correlation would require Coefficient improvements in the Sig. (2environment in which I tailed) live. N When I’m ill, my Correlation recovery is influenced Coefficient by the quality and Sig. (2comfort of my tailed) surrounding. N **. Correlation is significant at the 0.01 level (2-tailed).

.

.000

.801

.109

206 .640**

206 1.000

206 -.040

206 -.106

.000

.

.567

.129

206 -.018

206 -.040

206 1.000

206 .360**

.801

.567

.

.000

206 -.112

206 -.106

206 .360**

206 1.000

.109

.129

.000

.

206

206

206

206


102

Table C8 Acculturation and health beliefs correlations My state of health at any time is I usually expect to considerably influenced by whether

Spearman's Mainstream Acculturation Score

Correlation

rho

Coefficient

Mainstream

Heritage

take medicine to

or not I’ve been exposed to

Acculturation

Acculturation

help me recover

infectious or contagious disease

Score

Score

from illness.

organisms.

**

-.029

-.207**

.

.000

.676

.003

206

206

206

206

.640**

1.000

.003

-.123

.000

.

.970

.078

206

206

206

206

-.029

.003

1.000

.196**

.676

.970

.

.005

206

1.000

Sig. (2-

.640

tailed) N Heritage Acculturation Score

Correlation Coefficient Sig. (2tailed) N

I usually expect to take medicine to

Correlation

help me recover from illness.

Coefficient Sig. (2tailed) N

My state of health at any time is

Correlation

considerably influenced by whether

Coefficient

or not I’ve been exposed to

Sig. (2-

infectious or contagious disease

tailed)

organisms.

N

**. Correlation is significant at the 0.01 level (2-tailed).

206

206

206

**

-.123

**

1.000

.003

.078

.005

.

206

206

206

206

-.207

.196


103

Table D8 Acculturation and health beliefs correlations

Spearman's Mainstream rho Acculturation Score

Mainstream Heritage Acculturation Acculturation Score Score 1.000 .640**

Correlation Coefficient Sig. (2tailed) N Heritage Acculturation Correlation Score Coefficient Sig. (2tailed) N God has given me the Correlation means by which to Coefficient improve my health. Sig. (2tailed) N How well or badly I Correlation look after myself Coefficient generally has an Sig. (2influence on my overall tailed) health. N **. Correlation is significant at the 0.01 level (2-tailed).

God has given How well or badly I me the means look after myself by which to generally has an improve my influence on my health. overall health. -.051 -.092

.

.000

.464

.188

206 .640**

206 1.000

206 -.100

206 -.120

.000

.

.154

.085

206 -.051

206 -.100

206 1.000

206 .299**

.464

.154

.

.000

206 -.092

206 -.120

206 .299**

206 1.000

.188

.085

.000

.

206

206

206

206


104

Table E8 Acculturation and health beliefs correlations The speed of my recovery

Mainstream

Heritage

Acculturation Acculturation Score Spearman's Mainstream

Correlation

rho

Acculturation

Coefficient

Score

Sig. (2-

Score

from an

I see illness as

infection

a challenge to

depends a lot

be

on the

overcome—a

virulence of

determined

the disease

attitude on my

organisms

part can speed

causing it.

my recovery.

**

-.051

-.170*

.

.000

.471

.014

206

206

206

206

.640**

1.000

-.045

-.135

.000

.

.517

.054

206

206

206

206

-.051

-.045

1.000

.248**

.471

.517

.

.000

206

206

206

206

-.170*

-.135

.248**

1.000

.014

.054

.000

.

206

206

206

206

1.000

.640

tailed) N Heritage

Correlation

Acculturation

Coefficient

Score

Sig. (2tailed) N

The speed of my Correlation recovery from

Coefficient

an infection

Sig. (2-

depends a lot on tailed) the virulence of

N

the disease organisms causing it. I see illness as a Correlation challenge to be

Coefficient

overcome—a

Sig. (2-

determined

tailed)

attitude on my

N

part can speed my recovery. **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).


105

Table 9 Acculturation descriptives Minimu m

Maximu m

Std. Deviatio Varianc n e

N Range Mean Statisti Statisti Statisti Std. c c Statistic Statistic c Error Statistic Statistic Mainstream 206 7.90 1.10 9.00 6.6689 .1083 1.55457 2.417 Acculturatio 1 n Score Heritage 206 7.90 1.10 9.00 5.9811 .1092 1.56787 2.458 Acculturatio 4 n Score Valid N 206 (listwise)


106

Table A10 Immigrant generation and health beliefs correlation When I’m ill enough to I believe I may consult a have certain in doctor, my built recovery will weaknesses be faster if I which make me comply vulnerable to properly with particular the advice and illness or treatment I disorders. get.

Generational Status: Of the following definitions for generational status please choose which applies most to you. Spearman's Generational rho Status: Of the following definitions for generational status please choose which applies most to you.

1.000

-.004

.139*

.

.959

.047

206

206

206

-.004

1.000

.142*

Sig. (2tailed)

.959

.

.042

N

206

206

206

.139*

.142*

1.000

Sig. (2tailed)

.047

.042

.

N

206

206

206

Correlation Coefficient Sig. (2tailed) N

I believe I may have certain in built weaknesses which make me vulnerable to particular illness or disorders.

Correlation Coefficient

When I’m ill enough to consult a doctor, my recovery will be faster if I comply properly with the advice and treatment I get.


*. Correlation is significant at the 0.05 level (2-tailed).


107

Table B10 Immigrant generation and health beliefs correlations. Generational Status: Of the following To improve definitions for my health generational would require status please improvements choose which in the applies most environment in to you. which I live. Spearman's Generational Correlation 1.000 .112 rho Status: Of the Coefficient following Sig. (2. .109 definitions for tailed) generational N 206 206 status please choose which applies most to you. To improve my Correlation .112 1.000 health would Coefficient require Sig. (2.109 . improvements tailed) in the N 206 206 environment in which I live. When I’m ill, Correlation .096 .360** my recovery is Coefficient influenced by Sig. (2.169 .000 the quality and tailed) comfort of my N 206 206 surrounding. **. Correlation is significant at the 0.01 level (2-tailed).

When I’m ill, my recovery is influenced by the quality and comfort of my surrounding. .096 .169 206

.360** .000 206

1.000 . 206


108

Table C10 Immigrant generation and health beliefs correlations. My state of health at any time is I usually considerably expect to influenced by take whether or not medicine I’ve been exposed to help me to infectious or recover contagious from disease illness. organisms.

Generational Status: Of the following definitions for generational status please choose which applies most to you. 1.000

.205**

.104

.

.003

.138

206

206

206

.205**

1.000

.196**

Sig. (2tailed)

.003

.

.005

N

206

206

206


.104

.196**

1.000

Sig. (2tailed)

.138

.005

.

N

206

206

206

Spearman's Generational rho Status: Of the following definitions for generational status please choose which applies most to you.


I usually expect to take medicine to help me recover from illness.


My state of health at any time is considerably influenced by whether or not I’ve been exposed to infectious or contagious disease organisms.

Sig. (2tailed) N



109

Table D10 Immigrant generation and health beliefs correlations. Generational Status: Of the following definitions for generational status please choose which applies most to you. Spearman's Generational Correlation 1.000 rho Status: Of the Coefficient following Sig. (2. definitions for tailed) generational N 206 status please choose which applies most to you. God has given Correlation .029 me the means by Coefficient which to Sig. (2.676 improve my tailed) health. N 206 How well or Correlation -.009 badly I look after Coefficient myself generally Sig. (2.900 has an influence tailed) on my overall N 206 health. **. Correlation is significant at the 0.01 level (2-tailed).

God has given me the means by which to improve my health. .029

How well or badly I look after myself generally has an influence on my overall health. -.009

.676

.900

206

206

1.000

.299**

.

.000

206 .299**

206 1.000

.000

.

206

206


110

Table E10 Immigrant generation and health belief correlations. Generational Status: Of the following definitions for generational status please choose which applies most to you. Correlation 1.000 Coefficient Sig. (2. tailed) N 206

Spearman's Generational rho Status: Of the following definitions for generational status please choose which applies most to you. The speed of Correlation -.028 my recovery Coefficient from an Sig. (2.693 infection tailed) depends a lot N 206 on the virulence of the disease organisms causing it. I see illness as Correlation .040 a challenge to Coefficient be overcome— Sig. (2.571 a determined tailed) attitude on my N 206 part can speed my recovery. **. Correlation is significant at the 0.01 level (2-tailed).

The speed of my recovery from an infection depends a lot on the virulence of the disease organisms causing it. -.028

I see illness as a challenge to be overcome—a determined attitude on my part can speed my recovery. .040

.693

.571

206

206

1.000

.248**

.

.000

206

206

.248**

1.000

.000

.

206

206


111

Table 11 Highest degree earned, acculturation, and immigrant generation correlations. Generational Status: Of the following definitions for generational status please choose which Highest Mainstream Heritage applies most degree Acculturation Acculturation to you. earned: Score Score Spearman's Generational rho Status: Of the following definitions for generational status please choose which applies most to you.


1.000

-.053

-.087

-.080

.

.449

.216

.253

206

206

206

206

-.053

1.000

-.107

-.038

Sig. (2tailed)

.449

.

.126

.587

N

206

206

206

206

-.087

-.107

1.000

.640**

Sig. (2tailed)

.216

.126

.

.000

N

206

206

206

206

-.080

-.038

**

1.000

Sig. (2tailed)

.253

.587

.000

.

N

206

206

206

206

Sig. (2tailed) N

Highest degree Correlation earned: Coefficient

Mainstream Acculturation Score

Heritage Acculturation Score




.640


112

Table A12 Generational Status, MA, and In-built Weakness Health Belief Case Processing Summary N Marginal Percentage I believe I may have Strongly agree 14 6.8% certain in built Agree 38 18.4% weaknesses which Somewhat agree 66 32.0% make me vulnerable to Neither agree nor disagree 19 9.2% particular illness or Somewhat disagree 15 7.3% disorders. Disagree 39 18.9% Strongly disagree 15 7.3% Generational Status: Of First-generation immigrant 8 3.9% the following (neither you or your parents definitions for were generational status Second-generation 24 11.7% please choose which immigrant (at least one of applies most to you. your parents wa Third-generation immigrant 174 84.5% or more (both of your parents are Valid 206 100.0% Missing 0 Total 206 Table B12 Generational Status, MA, and In-built Weakness Health Belief Regression Model Fitting Information -2 Log Model Likelihood Chi-Square df Sig. Intercept Only 512.562 Final 504.427 8.136 6 .228 Link function: Logit.


Table C12 Generational Status, MA, and In-built Weakness Health Belief Pseudo R-Square Cox and Snell .039 Nagelkerke .040 McFadden .011 Link function: Logit.

113


114

Table D12 Generational Status, MA, and In-built Weakness Health Belief Parameter Estimates

Threshold

Estimate -2.553 -.790 .765 1.212 1.610 3.365 .065 -.613 .268 0a -.680 -.349 0a .027

[q0010 = 1.00] [q0010 = 2.00] [q0010 = 3.00] [q0010 = 4.00] [q0010 = 5.00] [q0010 = 6.00] Location MainAccScore [q0006=1.00] [q0006=2.00] [q0006=3.00] Scale [q0006=1.00] [q0006=2.00] [q0006=3.00] MainAccScore Link function: Logit. a. This parameter is set to zero because it is redundant.

Std. Error .953 .621 .611 .666 .731 1.138 .086 .483 .358 . .339 .203 . .042

Wald 7.172 1.618 1.568 3.314 4.843 8.737 .566 1.611 .561 . 4.030 2.960 . .419

df 1 1 1 1 1 1 1 1 1 0 1 1 0 1

Sig. .007 .203 .211 .069 .028 .003 .452 .204 .454 . .045 .085 . .517

95% Confidence Interval Lower Bound Upper Bound -4.421 -.684 -2.008 .428 -.432 1.962 -.093 2.517 .176 3.043 1.134 5.596 -.104 .233 -1.559 .333 -.434 .971 . . -1.344 -.016 -.747 .049 . . -.055 .109


Table A13 Generational Status, MA, and Doctor’s Advice Health Belief Case Processing Summary Marginal N Percentage When I’m ill enough to Strongly agree 55 26.7% consult a doctor, my Agree 101 49.0% recovery will be faster Somewhat agree 41 19.9% if I comply properly Neither agree nor 6 2.9% with the advice and disagree treatment I get. Somewhat disagree 3 1.5% Generational Status: Of First-generation 8 3.9% the following immigrant (neither you definitions for or your parents were generational status Second-generation 24 11.7% please choose which immigrant (at least one applies most to you. of your parents wa Third-generation 174 84.5% immigrant or more (both of your parents are Valid 206 100.0% Missing 0 Total 206

115


116

Table B13 Generational Status, MA, and Doctor’s Advice Health Belief Regression Model Fitting Information -2 Log Model Likelihood Chi-Square df Intercept Only 337.038 Final .000 337.038 6 Link function: Logit.

Table C13 Generational Status, MA, and Doctor’s Advice Health Belief Pseudo R-Square Cox and Snell .805 Nagelkerke .888 McFadden .689 Link function: Logit.

Sig. .000


117

Table D13 Generational Status, MA, and Doctor’s Advice Health Belief Parameter Estimates

Threshold

Estimate -.825 2.238 5.097 6.874 .118 -1.703 -.684 0a -2.324 .356 0a .046

[q0011 = 1.00] [q0011 = 2.00] [q0011 = 3.00] [q0011 = 4.00] Location MainAccScore [q0006=1.00] [q0006=2.00] [q0006=3.00] Scale [q0006=1.00] [q0006=2.00] [q0006=3.00] MainAccScore Link function: Logit. a. This parameter is set to zero because it is redundant.

Std. Error .814 1.039 1.884 2.550 .119 .607 .792 . 1.392 .249 . .048

Wald 1.029 4.639 7.321 7.264 .977 7.881 .745 . 2.788 2.044 . .905

df 1 1 1 1 1 1 1 0 1 1 0 1

Sig. .310 .031 .007 .007 .323 .005 .388 . .095 .153 . .342

95% Confidence Interval Lower Bound Upper Bound -2.420 .769 .202 4.275 1.405 8.789 1.875 11.872 -.116 .351 -2.892 -.514 -2.235 .868 . . -5.052 .404 -.132 .843 . . -.049 .140

Running head: ACCULTURATION AND HEALTH BELIEFS Table A14 Generational Status, MA, and Environmental Improvement Health Belief Case Processing Summary Marginal N Percentage To improve my health Strongly agree 26 12.6% would require Agree 45 21.8% improvements in the Somewhat agree 60 29.1% environment in which I Neither agree nor 34 16.5% live. disagree Somewhat disagree 11 5.3% Disagree 24 11.7% Strongly disagree 6 2.9% Generational Status: Of First-generation 8 3.9% the following immigrant (neither you definitions for or your parents were generational status Second-generation 24 11.7% please choose which immigrant (at least one applies most to you. of your parents wa Third-generation 174 84.5% immigrant or more (both of your parents are Valid 206 100.0% Missing 0 Total 206

118

ACCULTURATION AND HEALTH BELIEFS Table B14 Generational Status, MA, and Environmental Improvement Health Belief Model Fitting Information -2 Log Model Likelihood Chi-Square df Sig. Intercept Only 509.960 Final 493.770 16.189 6 .013 Link function: Logit.

Table C14 Generational Status, MA, and Environmental Improvement Health Belief Pseudo RSquare Cox and Snell .076 Nagelkerke .078 McFadden .022 Link function: Logit.

119


120

Table D14 Generational Status, MA, and Environmental Improvement Health Belief Parameter Estimates

Threshold

Estimate -1.309 -.540 .191 .698 .932 2.031 -.013 -1.079 -.022 0a -.736 -.270 0a -.073


Std. Error .535 .398 .365 .414 .452 .704 .050 .388 .188 . .448 .200 . .043

Wald 5.977 1.844 .273 2.847 4.256 8.333 .065 7.729 .014 . 2.696 1.831 . 2.867

df 1 1 1 1 1 1 1 1 1 0 1 1 0 1

Sig. .014 .174 .602 .092 .039 .004 .799 .005 .905 . .101 .176 . .090

95% Confidence Interval Lower Bound Upper Bound -2.358 -.260 -1.320 .240 -.525 .906 -.113 1.510 .047 1.817 .652 3.410 -.112 .086 -1.840 -.318 -.391 .346 . . -1.615 .143 -.661 .121 . . -.157 .011

Running head: ACCULTURATION AND HEALTH BELIEFS Table A15 Generational Status, MA, and Quality of Surroundings Health Belief Case Processing Summary Marginal N Percentage When I’m ill, my Strongly agree 37 18.0% recovery is influenced Agree 85 41.3% by the quality and Somewhat agree 63 30.6% comfort of my Neither agree nor 9 4.4% surrounding. disagree Somewhat disagree 8 3.9% Disagree 3 1.5% Strongly disagree 1 0.5% Generational Status: Of First-generation 8 3.9% the following immigrant (neither you definitions for or your parents were generational status Second-generation 24 11.7% please choose which immigrant (at least one applies most to you. of your parents wa Third-generation 174 84.5% immigrant or more (both of your parents are Valid 206 100.0% Missing 0 Total 206 Table B15 Generational Status, MA, and Quality of Surroundings Health Belief Regression Model Fitting Information -2 Log Model Likelihood Chi-Square df Sig. Intercept Only 398.026 Final .000 398.026 6 .000 Link function: Logit.

121


Table C15 Generational Status, MA, and Quality of Surroundings Health Belief Pseudo RSquare Cox and Snell .855 Nagelkerke .912 McFadden .697 Link function: Logit.

122


123

Table D15 Generational Status, MA, and Quality of Surroundings Health Belief Parameter Estimates

Threshold

Estimate -2.790 -.481 1.778 2.528 3.957 5.795 -.130 -1.692 -.095 0a -2.046 -.152 0a .035


Std. Error 1.079 .668 .839 1.006 1.423 2.191 .105 .617 .449 . 1.402 .214 . .044

Wald 6.686 .519 4.489 6.310 7.737 6.997 1.526 7.507 .045 . 2.132 .510 . .646

df 1 1 1 1 1 1 1 1 1 0 1 1 0 1

Sig. .010 .471 .034 .012 .005 .008 .217 .006 .833 . .144 .475 . .422

95% Confidence Interval Lower Bound Upper Bound -4.904 -.675 -1.791 .828 .133 3.422 .556 4.501 1.169 6.745 1.501 10.089 -.335 .076 -2.902 -.482 -.974 .784 . . -4.794 .701 -.571 .266 . . -.051 .121

ACCULTURATION AND HEALTH BELIEFS Table A16 Generational Status, MA, and Medication Health Belief Case Processing Summary Marginal N Percentage I usually expect to take Strongly agree 9 4.4% medicine to help me Agree 35 17.0% recover from illness. Somewhat agree 56 27.2% Neither agree nor 29 14.1% disagree Somewhat disagree 36 17.5% Disagree 32 15.5% Strongly disagree 9 4.4% Generational Status: Of First-generation 8 3.9% the following immigrant (neither you definitions for or your parents were generational status Second-generation 24 11.7% please choose which immigrant (at least one applies most to you. of your parents wa Third-generation 174 84.5% immigrant or more (both of your parents are Valid 206 100.0% Missing 0 Total 206 Table B16 Generational Status, MA, and Medication Health Belief Regression Model Fitting Information -2 Log Model Likelihood Chi-Square df Sig. Intercept Only 511.624 Final 501.121 10.503 6 .105 Link function: Logit.

124

ACCULTURATION AND HEALTH BELIEFS Table C16 Generational Status, MA, and Medication Health Belief Pseudo R-Square Cox and Snell .050 Nagelkerke .051 McFadden .014 Link function: Logit.

125


126

Table D16 Generational Status, MA, and Medication Health Belief Parameter Estimates

Threshold

Estimate -4.259 -1.882 -.190 .579 1.753 4.003 .012 -1.880 -1.112 0a -.120 -.047 0a .041


Std. Error 1.427 .849 .656 .671 .816 1.344 .097 .944 .582 . .343 .202 . .041

Wald 8.906 4.911 .084 .745 4.617 8.877 .015 3.969 3.655 . .122 .053 . 1.004

df 1 1 1 1 1 1 1 1 1 0 1 1 0 1

Sig. .003 .027 .772 .388 .032 .003 .902 .046 .056 . .727 .818 . .316

95% Confidence Interval Lower Bound Upper Bound -7.057 -1.462 -3.546 -.217 -1.476 1.095 -.736 1.894 .154 3.352 1.370 6.636 -.178 .202 -3.730 -.030 -2.252 .028 . . -.791 .552 -.443 .349 . . -.039 .122

Running head: ACCULTURATION AND HEALTH BELIEFS Table A17 Generational Status, MA, and Infection Exposure Health Belief Case Processing Summary Marginal N Percentage My state of health at Strongly agree 27 13.1% any time is Agree 62 30.1% considerably influenced Somewhat agree 54 26.2% by whether or not I’ve Neither agree nor 28 13.6% been exposed to disagree infectious or contagious Somewhat disagree 21 10.2% disease organisms. Disagree 12 5.8% Strongly disagree 2 1.0% Generational Status: Of First-generation 8 3.9% the following immigrant (neither you definitions for or your parents were generational status Second-generation 24 11.7% please choose which immigrant (at least one applies most to you. of your parents wa Third-generation 174 84.5% immigrant or more (both of your parents are Valid 206 100.0% Missing 0 Total 206 Table B17 Generational Status, MA, and Infection Exposure Health Belief Model Fitting Information -2 Log Model Likelihood Chi-Square df Sig. Intercept Only 487.067 Final 481.436 5.631 6 .466 Link function: Logit.

127

ACCULTURATION AND HEALTH BELIEFS Table C17 Generational Status, MA, and Infection Exposure Health Belief Pseudo R-Square Cox and Snell .027 Nagelkerke .028 McFadden .008 Link function: Logit.

128


129

Table D17 Generational Status, MA, and Infection Exposure Health Belief Parameter Estimates

Threshold

Estimate -2.967 -1.329 -.226 .538 1.560 3.560 -.148 -.810 -.354 0a .110 .067 0a -.004


Std. Error 1.041 .674 .552 .567 .708 1.270 .091 .747 .417 . .365 .208 . .042

Wald 8.123 3.888 .167 .899 4.857 7.858 2.635 1.176 .722 . .092 .104 . .007

df 1 1 1 1 1 1 1 1 1 0 1 1 0 1

Sig. .004 .049 .683 .343 .028 .005 .104 .278 .395 . .762 .747 . .933

95% Confidence Interval Lower Bound Upper Bound -5.007 -.927 -2.649 -.008 -1.308 .857 -.574 1.650 .173 2.947 1.071 6.048 -.327 .031 -2.274 .654 -1.171 .463 . . -.604 .825 -.340 .474 . . -.086 .079

Running head: ACCULTURATION AND HEALTH BELIEFS Table A18 Generational Status, MA, and God Provides Health Belief Case Processing Summary Marginal N Percentage God has given me the Strongly agree 60 29.1% means by which to Agree 88 42.7% improve my health. Somewhat agree 33 16.0% Neither agree nor 17 8.3% disagree Somewhat disagree 2 1.0% Disagree 6 2.9% Generational Status: Of First-generation 8 3.9% the following immigrant (neither you definitions for or your parents were generational status Second-generation 24 11.7% please choose which immigrant (at least one applies most to you. of your parents wa Third-generation 174 84.5% immigrant or more (both of your parents are Valid 206 100.0% Missing 0 Total 206 Table B18 Generational Status, MA, and God Provides Health Belief Model Fitting Information -2 Log Model Likelihood Chi-Square df Sig. Intercept Only 384.944 Final 371.662 13.283 6 .039 Link function: Logit.

130


Table C18 Generational Status, MA, and God Provides Health Belief Pseudo R-Square Cox and Snell .062 Nagelkerke .067 McFadden .024 Link function: Logit.

131


132

Table D18 Generational Status, MA, and God Provides Health Belief Parameter Estimates

Threshold

Estimate -2.246 2.964 6.095 10.260 11.455 .070 -1.715 -.679 0a -.206 .463 0a .154

[q0016 = 1.00] [q0016 = 2.00] [q0016 = 3.00] [q0016 = 4.00] [q0016 = 5.00] Location MainAccScore [q0006=1.00] [q0006=2.00] [q0006=3.00] Scale [q0006=1.00] [q0006=2.00] [q0006=3.00] MainAccScore Link function: Logit. a. This parameter is set to zero because it is redundant.

Std. Error 1.449 1.545 2.351 3.772 4.230 .201 1.728 1.625 . .421 .255 . .048

Wald 2.403 3.679 6.723 7.397 7.333 .122 .985 .174 . .240 3.291 . 10.373

df 1 1 1 1 1 1 1 1 0 1 1 0 1

Sig. .121 .055 .010 .007 .007 .727 .321 .676 . .624 .070 . .001

95% Confidence Interval Lower Bound Upper Bound -5.087 .594 -.065 5.993 1.488 10.702 2.866 17.654 3.164 19.746 -.324 .465 -5.103 1.672 -3.864 2.506 . . -1.032 .619 -.037 .964 . . .060 .247

Running head: ACCULTURATION AND HEALTH BELIEFS Table A19 Generational Status, MA, and Self-care Health Belief Case Processing Summary Marginal N Percentage How well or badly I Strongly agree 95 46.1% look after myself Agree 87 42.2% generally has an Somewhat agree 17 8.3% influence on my overall Neither agree nor 4 1.9% health. disagree Disagree 3 1.5% Generational Status: Of First-generation 8 3.9% the following immigrant (neither you definitions for or your parents were generational status Second-generation 24 11.7% please choose which immigrant (at least one applies most to you. of your parents wa Third-generation 174 84.5% immigrant or more (both of your parents are Valid 206 100.0% Missing 0 Total 206 Table B19 Generational Status, MA, and Self-care Health Belief Regression Model Fitting Information -2 Log Model Likelihood Chi-Square df Sig. Intercept Only 295.144 Final 294.642 .502 6 .998 Link function: Logit.

133


Table C19 Generational Status, MA, and Self-care Health Belief Pseudo R-Square Cox and Snell .002 Nagelkerke .003 McFadden .001 Link function: Logit.

134


135

Table D19 Generational Status, MA, and Self-care Health Belief Parameter Estimates

Threshold

Estimate -.385 1.554 2.732 3.503 -.038 -.088 .082 0a -.027 .123 0a -.020

[q0017 = 1.00] [q0017 = 2.00] [q0017 = 3.00] [q0017 = 4.00] Location MainAccScore [q0006=1.00] [q0006=2.00] [q0006=3.00] Scale [q0006=1.00] [q0006=2.00] [q0006=3.00] MainAccScore Link function: Logit. a. This parameter is set to zero because it is redundant.

Std. Error .595 .735 1.101 1.394 .084 .607 .410 . .484 .271 . .056

Wald .418 4.475 6.160 6.315 .202 .021 .040 . .003 .207 . .129

df 1 1 1 1 1 1 1 0 1 1 0 1

Sig. .518 .034 .013 .012 .653 .885 .841 . .956 .650 . .720

95% Confidence Interval Lower Bound Upper Bound -1.551 .782 .114 2.994 .575 4.889 .771 6.236 -.201 .126 -1.277 1.102 -.721 .885 . . -.975 .922 -.409 .655 . . -.130 .090

Running head: ACCULTURATION AND HEALTH BELIEFS Table A20 Generational Status, MA, and Virulence Health Belief Case Processing Summary Marginal N Percentage The speed of my Strongly agree 22 10.7% recovery from an Agree 96 46.6% infection depends a lot Somewhat agree 54 26.2% on the virulence of the Neither agree nor 26 12.6% disease organisms disagree causing it. Somewhat disagree 7 3.4% Disagree 1 0.5% Generational Status: Of First-generation 8 3.9% the following immigrant (neither you definitions for or your parents were generational status Second-generation 24 11.7% please choose which immigrant (at least one applies most to you. of your parents wa Third-generation 174 84.5% immigrant or more (both of your parents are Valid 206 100.0% Missing 0 Total 206 Table B20 Generational Status, MA, and Virulence Health Belief Regression Model Fitting Information -2 Log Model Likelihood Chi-Square df Sig. Intercept Only 376.811 Final 372.975 3.836 6 .699 Link function: Logit.

136

ACCULTURATION AND HEALTH BELIEFS Table C20 Generational Status, MA, and Virulence Health Belief Pseudo R-Square Cox and Snell .018 Nagelkerke .020 McFadden .007 Link function: Logit.

137


138

Table D20 Generational Status, MA, and Virulence Health Belief Parameter Estimates

Threshold

Estimate -2.419 .060 1.451 3.106 5.297 -.040 -.572 .494 0a -.359 .011 0a .005

[q0018 = 1.00] [q0018 = 2.00] [q0018 = 3.00] [q0018 = 4.00] [q0018 = 5.00] Location MainAccScore [q0006=1.00] [q0006=2.00] [q0006=3.00] Scale [q0006=1.00] [q0006=2.00] [q0006=3.00] MainAccScore Link function: Logit. a. This parameter is set to zero because it is redundant.

Std. Error .957 .582 .701 1.077 1.880 .087 .585 .440 . .350 .207 . .043

Wald 6.395 .011 4.288 8.322 7.935 .213 .958 1.262 . 1.053 .003 . .015

df 1 1 1 1 1 1 1 1 0 1 1 0 1

Sig. .011 .918 .038 .004 .005 .645 .328 .261 . .305 .957 . .903

95% Confidence Interval Lower Bound Upper Bound -4.294 -.544 -1.081 1.201 .078 2.824 .996 5.216 1.612 8.983 -.210 .130 -1.719 .574 -.368 1.356 . . -1.046 .327 -.395 .417 . . -.079 .089

Running head: ACCULTURATION AND HEALTH BELIEFS Table A21 Generational Status, MA, and Illness as a Challenge Health Belief Case Processing Summary Marginal N Percentage I see illness as a Strongly agree 34 16.5% challenge to be Agree 58 28.2% overcome—a Somewhat agree 73 35.4% determined attitude on Neither agree nor 19 9.2% my part can speed my disagree recovery. Somewhat disagree 16 7.8% Disagree 4 1.9% Strongly disagree 2 1.0% Generational Status: Of First-generation 8 3.9% the following immigrant (neither you definitions for or your parents were generational status Second-generation 24 11.7% please choose which immigrant (at least one applies most to you. of your parents wa Third-generation 174 84.5% immigrant or more (both of your parents are Valid 206 100.0% Missing 0 Total 206

Table B21 Generational Status, MA, and Illness as a Challenge Health Belief Regression Model Fitting Information -2 Log Model Likelihood Chi-Square df Sig. Intercept Only 440.922 Final 434.028 6.894 6 .331 Link function: Logit.

139


Table C21 Generational Status, MA, and Illness as a Challenge Health Belief Pseudo R-Square Cox and Snell .033 Nagelkerke .034 McFadden .011 Link function: Logit.

140


141

Table D21 Generational Status, MA, and Illness as a Challenge Health Belief Parameter Estimates

Threshold

Estimate -2.502 -1.085 .517 1.241 2.614 3.735 -.129 -1.124 .198 0a -.015 -.145 0a -8.710E-5


Std. Error .920 .631 .563 .650 .954 1.321 .088 .739 .354 . .411 .202 . .042

Wald 7.390 2.953 .845 3.647 7.512 7.996 2.144 2.316 .312 . .001 .512 . .000

df 1 1 1 1 1 1 1 1 1 0 1 1 0 1

Sig. .007 .086 .358 .056 .006 .005 .143 .128 .576 . .970 .474 . .998

95% Confidence Interval Lower Bound Upper Bound -4.306 -.698 -2.322 .153 -.585 1.620 -.033 2.515 .745 4.484 1.146 6.324 -.301 .044 -2.572 .324 -.496 .892 . . -.820 .790 -.541 .251 . . -.083 .083

Running head: ACCULTURATION AND HEALTH BELIEFS Table A22 Generational Status, HA, and In-built Weakness Health Belief Case Processing Summary Marginal N Percentage I believe I may have Strongly agree 14 6.8% certain in built Agree 38 18.4% weaknesses which Somewhat agree 66 32.0% make me vulnerable to Neither agree nor 19 9.2% particular illness or disagree disorders. Somewhat disagree 15 7.3% Disagree 39 18.9% Strongly disagree 15 7.3% Generational Status: Of First-generation 8 3.9% the following immigrant (neither you definitions for or your parents were generational status Second-generation 24 11.7% please choose which immigrant (at least one applies most to you. of your parents wa Third-generation 174 84.5% immigrant or more (both of your parents are Valid 206 100.0% Missing 0 Total 206 Table B22. Generational Status, HA, and In-built Weakness Health Belief Regression Model Fitting Information -2 Log Model Likelihood Chi-Square df Sig. Intercept Only 513.234 Final 505.324 7.911 5 .161 Link function: Logit.

142

ACCULTURATION AND HEALTH BELIEFS Table C22. Generational Status, HA, and In-built Weakness Health Belief Pseudo R-Square Cox and Snell .038 Nagelkerke .039 McFadden .011 Link function: Logit.

143


144

Table D22. Generational Status, HA, and In-built Weakness Health Belief Parameter Estimates

Threshold

Estimate -2.146 -.665 .641 1.014 1.345 2.800 .060 -.537 .204 0a -.674 -.340 0a

[q0010 = 1.00] [q0010 = 2.00] [q0010 = 3.00] [q0010 = 4.00] [q0010 = 5.00] [q0010 = 6.00] Location HerAccScore [q0006=1.00] [q0006=2.00] [q0006=3.00] Scale [q0006=1.00] [q0006=2.00] [q0006=3.00] Link function: Logit. a. This parameter is set to zero because it is redundant.

Std. Error .511 .463 .463 .467 .472 .521 .073 .376 .294 . .339 .203 .

Wald 17.624 2.061 1.918 4.724 8.131 28.860 .677 2.042 .478 . 3.945 2.822 .

df 1 1 1 1 1 1 1 1 1 0 1 1 0

Sig. .000 .151 .166 .030 .004 .000 .410 .153 .489 . .047 .093 .

95% Confidence Interval Lower Bound Upper Bound -3.147 -1.144 -1.572 .243 -.266 1.548 .100 1.929 .420 2.269 1.778 3.821 -.083 .204 -1.274 .200 -.374 .781 . . -1.339 -.009 -.737 .057 . .


145

Table A23. Generational Status, HA, and Doctor’s Advice Health Belief Case Processing Summary Marginal N Percentage When I’m ill enough to Strongly agree 55 26.7% consult a doctor, my Agree 101 49.0% recovery will be faster Somewhat agree 41 19.9% if I comply properly Neither agree nor 6 2.9% with the advice and disagree treatment I get. Somewhat disagree 3 1.5% Generational Status: Of First-generation 8 3.9% the following immigrant (neither you definitions for or your parents were generational status Second-generation 24 11.7% please choose which immigrant (at least one applies most to you. of your parents wa Third-generation 174 84.5% immigrant or more (both of your parents are Valid 206 100.0% Missing 0 Total 206 Table B23. Generational Status, HA, and Doctor’s Advice Health Belief Regression Model Fitting Information -2 Log Model Likelihood Chi-Square df Intercept Only 330.908 Final 316.943 13.965 5 Link function: Logit.

Sig. .016

ACCULTURATION AND HEALTH BELIEFS Table C23. Generational Status, HA, and Doctor’s Advice Health Belief Pseudo R-Square Cox and Snell .066 Nagelkerke .072 McFadden .029 Link function: Logit.

146


147

Table D23. Generational Status, HA, and Doctor’s Advice Health Belief Parameter Estimates

Threshold

Estimate -.232 2.059 4.130 5.364 .161 -1.437 -.450 0a -1.004 .348 0a

[q0011 = 1.00] [q0011 = 2.00] [q0011 = 3.00] [q0011 = 4.00] Location HerAccScore [q0006=1.00] [q0006=2.00] [q0006=3.00] Scale [q0006=1.00] [q0006=2.00] [q0006=3.00] Link function: Logit. a. This parameter is set to zero because it is redundant.

Std. Error .530 .556 .655 .844 .086 .406 .557 . 1.180 .245 .

Wald .191 13.720 39.774 40.425 3.464 12.506 .652 . .724 2.021 .

df 1 1 1 1 1 1 1 0 1 1 0

Sig. .662 .000 .000 .000 .063 .000 .420 . .395 .155 .

95% Confidence Interval Lower Bound Upper Bound -1.270 .807 .970 3.149 2.846 5.413 3.711 7.018 -.009 .330 -2.234 -.641 -1.542 .642 . . -3.317 1.309 -.132 .827 . .

Running head: ACCULTURATION AND HEALTH BELIEFS Table A24. Generational Status, HA, and Environmental Improvement Health Belief Case Processing Summary Marginal N Percentage To improve my health Strongly agree 26 12.6% would require Agree 45 21.8% improvements in the Somewhat agree 60 29.1% environment in which I Neither agree nor 34 16.5% live. disagree Somewhat disagree 11 5.3% Disagree 24 11.7% Strongly disagree 6 2.9% Generational Status: Of First-generation 8 3.9% the following immigrant (neither you definitions for or your parents were generational status Second-generation 24 11.7% please choose which immigrant (at least one applies most to you. of your parents wa Third-generation 174 84.5% immigrant or more (both of your parents are Valid 206 100.0% Missing 0 Total 206 Table B24. Generational Status, HA, and Environmental Improvement Health Belief Regression Model Fitting Information -2 Log Model Likelihood Chi-Square df Sig. Intercept Only 513.404 Final 499.854 13.550 5 .019 Link function: Logit.

148

ACCULTURATION AND HEALTH BELIEFS Table C24. Generational Status, HA, and Environmental Improvement Health Belief Pseudo RSquare Cox and Snell .064 Nagelkerke .066 McFadden .019 Link function: Logit.

149


150

Table D24. Generational Status, HA, and Environmental Improvement Health Belief Parameter Estimates

Threshold

Estimate -2.169 -.926 .267 1.087 1.459 3.188 -.035 -1.693 .018 0a -.754 -.265 0a


Std. Error .499 .469 .464 .473 .481 .602 .074 .379 .312 . .438 .199 .

Wald 18.907 3.888 .331 5.285 9.186 28.025 .223 19.926 .003 . 2.961 1.780 .

df 1 1 1 1 1 1 1 1 1 0 1 1 0

Sig. .000 .049 .565 .022 .002 .000 .637 .000 .954 . .085 .182 .

95% Confidence Interval Lower Bound Upper Bound -3.146 -1.191 -1.846 -.006 -.643 1.177 .160 2.014 .515 2.402 2.008 4.369 -.180 .110 -2.437 -.950 -.593 .629 . . -1.612 .105 -.655 .124 . .


Table A25. Generational Status, HA, and Quality of Surroundings Health Belief Case Processing Summary Marginal N Percentage When I’m ill, my Strongly agree 37 18.0% recovery is influenced Agree 85 41.3% by the quality and Somewhat agree 63 30.6% comfort of my Neither agree nor 9 4.4% surrounding. disagree Somewhat disagree 8 3.9% Disagree 3 1.5% Strongly disagree 1 0.5% Generational Status: Of First-generation 8 3.9% the following immigrant (neither you definitions for or your parents were generational status Second-generation 24 11.7% please choose which immigrant (at least one applies most to you. of your parents wa Third-generation 174 84.5% immigrant or more (both of your parents are Valid 206 100.0% Missing 0 Total 206 Table B25 Generational Status, HA, and Quality of Surroundings Health Belief Regression Model Fitting Information -2 Log Model Likelihood Chi-Square df Sig. Intercept Only 392.107 Final .000 392.107 5 .000 Link function: Logit.

151

ACCULTURATION AND HEALTH BELIEFS Table C25 Generational Status, HA, and Quality of Surroundings Health Belief Pseudo RSquare Cox and Snell .851 Nagelkerke .908 McFadden .686 Link function: Logit.

152


153

Table D25 Generational Status, HA, and Quality of Surroundings Health Belief Parameter Estimates

Threshold

Estimate -2.315 -.512 1.296 1.901 3.036 4.437 -.133 -1.305 -.104 0a -1.904 -.168 0a


Std. Error .530 .498 .517 .548 .680 1.099 .080 .289 .350 . 1.114 .214 .

Wald 19.105 1.055 6.288 12.029 19.931 16.306 2.730 20.394 .089 . 2.923 .618 .

df 1 1 1 1 1 1 1 1 1 0 1 1 0

Sig. .000 .304 .012 .001 .000 .000 .098 .000 .766 . .087 .432 .

95% Confidence Interval Lower Bound Upper Bound -3.352 -1.277 -1.488 .465 .283 2.309 .827 2.975 1.703 4.369 2.283 6.591 -.290 .025 -1.872 -.739 -.790 .582 . . -4.087 .279 -.587 .251 . .

ACCULTURATION AND HEALTH BELIEFS Table A26 Generational Status, HA, and Medication Health Belief Case Processing Summary Marginal N Percentage I usually expect to take Strongly agree 9 4.4% medicine to help me Agree 35 17.0% recover from illness. Somewhat agree 56 27.2% Neither agree nor 29 14.1% disagree Somewhat disagree 36 17.5% Disagree 32 15.5% Strongly disagree 9 4.4% Generational Status: Of First-generation 8 3.9% the following immigrant (neither you definitions for or your parents were generational status Second-generation 24 11.7% please choose which immigrant (at least one applies most to you. of your parents wa Third-generation 174 84.5% immigrant or more (both of your parents are Valid 206 100.0% Missing 0 Total 206

154

ACCULTURATION AND HEALTH BELIEFS Table B26 Generational Status, HA, and Medication Health Belief Regression Model Fitting Information -2 Log Model Likelihood Chi-Square df Sig. Intercept Only 529.743 Final 520.163 9.579 5 .088 Link function: Logit. Table C26 Generational Status, HA, and Medication Health Belief Pseudo R-Square Cox and Snell .045 Nagelkerke .047 McFadden .013 Link function: Logit.

155


156

Table D26 Generational Status, HA, and Medication Health Belief Parameter Estimates

Threshold

Estimate -3.338 -1.538 -.252 .334 1.223 2.923 -.007 -1.443 -.876 0a -.111 -.043 0a


Std. Error .602 .504 .489 .489 .499 .578 .078 .600 .380 . .342 .202 .

Wald 30.713 9.329 .266 .465 6.013 25.563 .009 5.773 5.313 . .105 .045 .

df 1 1 1 1 1 1 1 1 1 0 1 1 0

Sig. .000 .002 .606 .495 .014 .000 .924 .016 .021 . .746 .832 .

95% Confidence Interval Lower Bound Upper Bound -4.518 -2.157 -2.525 -.551 -1.210 .706 -.625 1.293 .245 2.200 1.790 4.055 -.161 .146 -2.619 -.266 -1.620 -.131 . . -.782 .560 -.439 .353 . .


Table A27 Generational Status, HA, and Infection Exposure Health Belief Case Processing Summary Marginal N Percentage My state of health at Strongly agree 27 13.1% any time is Agree 62 30.1% considerably influenced Somewhat agree 54 26.2% by whether or not I’ve Neither agree nor 28 13.6% been exposed to disagree infectious or contagious Somewhat disagree 21 10.2% disease organisms. Disagree 12 5.8% Strongly disagree 2 1.0% Generational Status: Of First-generation 8 3.9% the following immigrant (neither you definitions for or your parents were generational status Second-generation 24 11.7% please choose which immigrant (at least one applies most to you. of your parents wa Third-generation 174 84.5% immigrant or more (both of your parents are Valid 206 100.0% Missing 0 Total 206 Table B27 Generational Status, HA, and Infection Exposure Health Belief Regression Model Fitting Information -2 Log Model Likelihood Chi-Square df Sig. Intercept Only 493.178 Final 489.677 3.501 5 .623 Link function: Logit.

157

ACCULTURATION AND HEALTH BELIEFS Table C27 Generational Status, HA, and Infection Exposure Health Belief Pseudo R-Square Cox and Snell .017 Nagelkerke .017 McFadden .005 Link function: Logit.

158


159

Table D27 Generational Status, HA, and Infection Exposure Health Belief Parameter Estimates

Threshold

Estimate -2.464 -.801 .317 1.097 2.143 4.189 -.074 -.867 -.391 0a .141 .061 0a


Std. Error .538 .503 .500 .509 .549 .868 .080 .750 .412 . .366 .207 .

Wald 20.960 2.541 .402 4.641 15.234 23.301 .860 1.337 .900 . .149 .087 .

df 1 1 1 1 1 1 1 1 1 0 1 1 0

Sig. .000 .111 .526 .031 .000 .000 .354 .248 .343 . .700 .768 .

95% Confidence Interval Lower Bound Upper Bound -3.519 -1.409 -1.786 .184 -.663 1.296 .099 2.094 1.067 3.219 2.488 5.890 -.232 .083 -2.336 .603 -1.199 .417 . . -.576 .859 -.345 .468 . .

ACCULTURATION AND HEALTH BELIEFS Table A28 Generational Status, HA, and God Provides Health Belief Case Processing Summary Marginal N Percentage God has given me the Strongly agree 60 29.1% means by which to Agree 88 42.7% improve my health. Somewhat agree 33 16.0% Neither agree nor 17 8.3% disagree Somewhat disagree 2 1.0% Disagree 6 2.9% Generational Status: Of First-generation 8 3.9% the following immigrant (neither you definitions for or your parents were generational status Second-generation 24 11.7% please choose which immigrant (at least one applies most to you. of your parents wa Third-generation 174 84.5% immigrant or more (both of your parents are Valid 206 100.0% Missing 0 Total 206 Table B28 Generational Status, HA, and God Provides Health Belief Regression Model Fitting Information -2 Log Model Likelihood Chi-Square df Sig. Intercept Only 379.446 Final 373.266 6.180 5 .289 Link function: Logit.

160

ACCULTURATION AND HEALTH BELIEFS Table C28 Generational Status, HA, and God Provides Health Belief Pseudo R-Square Cox and Snell .030 Nagelkerke .032 McFadden .011 Link function: Logit.

161


162

Table D28 Generational Status, HA, and God Provides Health Belief Parameter Estimates

Threshold

Estimate -1.526 .405 1.517 2.908 3.274 -.094 -.541 -.055 0a -.002 .521 0a

[q0016 = 1.00] [q0016 = 2.00] [q0016 = 3.00] [q0016 = 4.00] [q0016 = 5.00] Location HerAccScore [q0006=1.00] [q0006=2.00] [q0006=3.00] Scale [q0006=1.00] [q0006=2.00] [q0006=3.00] Link function: Logit. a. This parameter is set to zero because it is redundant.

Std. Error .546 .532 .553 .650 .698 .086 .685 .644 . .418 .254 .

Wald 7.823 .577 7.528 20.027 21.993 1.199 .624 .007 . .000 4.227 .

df 1 1 1 1 1 1 1 1 0 1 1 0

Sig. .005 .447 .006 .000 .000 .274 .430 .932 . .997 .040 .

95% Confidence Interval Lower Bound Upper Bound -2.596 -.457 -.639 1.448 .433 2.601 1.635 4.182 1.906 4.642 -.263 .075 -1.884 .802 -1.317 1.207 . . -.821 .818 .024 1.018 . .

ACCULTURATION AND HEALTH BELIEFS Table A29 Generational Status, HA, and Self-care Health Belief Case Processing Summary Marginal N Percentage How well or badly I Strongly agree 95 46.1% look after myself Agree 87 42.2% generally has an Somewhat agree 17 8.3% influence on my overall Neither agree nor 4 1.9% health. disagree Disagree 3 1.5% Generational Status: Of First-generation 8 3.9% the following immigrant (neither you definitions for or your parents were generational status Second-generation 24 11.7% please choose which immigrant (at least one applies most to you. of your parents wa Third-generation 174 84.5% immigrant or more (both of your parents are Valid 206 100.0% Missing 0 Total 206 Table B29 Generational Status, HA, and Self-care Health Belief Regression Model Fitting Information -2 Log Model Likelihood Chi-Square df Sig. Intercept Only 286.744 Final 285.509 1.235 5 .941 Link function: Logit.

163

ACCULTURATION AND HEALTH BELIEFS Table C29 Generational Status, HA, and Self-care Health Belief Pseudo R-Square Cox and Snell .006 Nagelkerke .007 McFadden .003 Link function: Logit.

164


165

Table D29. Generational Status, HA, and Self-care Health Belief Parameter Estimates

Threshold

Estimate -.669 1.552 2.901 3.778 -.086 -.070 .074 0a -.056 .132 0a

[q0017 = 1.00] [q0017 = 2.00] [q0017 = 3.00] [q0017 = 4.00] Location HerAccScore [q0006=1.00] [q0006=2.00] [q0006=3.00] Scale [q0006=1.00] [q0006=2.00] [q0006=3.00] Link function: Logit. a. This parameter is set to zero because it is redundant.

Std. Error .538 .555 .648 .791 .087 .678 .474 . .481 .272 .

Wald 1.549 7.829 20.059 22.819 .991 .011 .024 . .013 .235 .

df 1 1 1 1 1 1 1 0 1 1 0

Sig. .213 .005 .000 .000 .320 .918 .876 . .908 .628 .

95% Confidence Interval Lower Bound Upper Bound -1.723 .385 .465 2.639 1.631 4.170 2.228 5.328 -.256 .083 -1.398 1.259 -.855 1.002 . . -.997 .886 -.402 .666 . .

ACCULTURATION AND HEALTH BELIEFS Table A30 Generational Status, HA, and Virulence Health Belief Case Processing Summary Marginal N Percentage The speed of my Strongly agree 22 10.7% recovery from an Agree 96 46.6% infection depends a lot Somewhat agree 54 26.2% on the virulence of the Neither agree nor 26 12.6% disease organisms disagree causing it. Somewhat disagree 7 3.4% Disagree 1 0.5% Generational Status: Of First-generation 8 3.9% the following immigrant (neither you definitions for or your parents were generational status Second-generation 24 11.7% please choose which immigrant (at least one applies most to you. of your parents wa Third-generation 174 84.5% immigrant or more (both of your parents are Valid 206 100.0% Missing 0 Total 206 Table B30 Generational Status, HA, and Virulence Health Belief Regression Model Fitting Information -2 Log Model Likelihood Chi-Square df Sig. Intercept Only 398.538 Final 394.816 3.722 5 .590 Link function: Logit.

166

ACCULTURATION AND HEALTH BELIEFS Table C30. Generational Status, HA, and Virulence Health Belief Pseudo R-Square Cox and Snell .018 Nagelkerke .019 McFadden .007 Link function: Logit.

167


168

Table D30 Generational Status, HA, and Virulence Health Belief Parameter Estimates

Threshold

Estimate -2.267 .126 1.470 3.070 5.183 -.032 -.552 .476 0a -.385 .023 0a

[q0018 = 1.00] [q0018 = 2.00] [q0018 = 3.00] [q0018 = 4.00] [q0018 = 5.00] Location HerAccScore [q0006=1.00] [q0006=2.00] [q0006=3.00] Scale [q0006=1.00] [q0006=2.00] [q0006=3.00] Link function: Logit. a. This parameter is set to zero because it is redundant.

Std. Error .546 .510 .525 .615 1.130 .082 .535 .406 . .351 .207 .

Wald 17.222 .061 7.847 24.890 21.026 .149 1.065 1.374 . 1.202 .013 .

df 1 1 1 1 1 1 1 1 0 1 1 0

Sig. .000 .805 .005 .000 .000 .699 .302 .241 . .273 .911 .

95% Confidence Interval Lower Bound Upper Bound -3.337 -1.196 -.874 1.125 .441 2.498 1.864 4.276 2.968 7.399 -.193 .129 -1.601 .496 -.320 1.272 . . -1.072 .303 -.382 .429 . .

ACCULTURATION AND HEALTH BELIEFS Table A31 Generational Status, HA, and Illness as a Challenge Health Belief Case Processing Summary Marginal N Percentage I see illness as a Strongly agree 34 16.5% challenge to be Agree 58 28.2% overcome—a Somewhat agree 73 35.4% determined attitude on Neither agree nor 19 9.2% my part can speed my disagree recovery. Somewhat disagree 16 7.8% Disagree 4 1.9% Strongly disagree 2 1.0% Generational Status: Of First-generation 8 3.9% the following immigrant (neither you definitions for or your parents were generational status Second-generation 24 11.7% please choose which immigrant (at least one applies most to you. of your parents wa Third-generation 174 84.5% immigrant or more (both of your parents are Valid 206 100.0% Missing 0 Total 206 Table B31 Generational Status, HA, and Illness as a Challenge Health Belief Regression Model Fitting Information -2 Log Model Likelihood Chi-Square df Sig. Intercept Only 447.586 Final 441.441 6.145 5 .292 Link function: Logit.

169

ACCULTURATION AND HEALTH BELIEFS Table C31 Generational Status, HA, and Illness as a Challenge Health Belief Pseudo R-Square Cox and Snell .029 Nagelkerke .031 McFadden .010 Link function: Logit.

170


171

Table D31 Generational Status, HA, and Illness as a Challenge Health Belief Parameter Estimates

Threshold

Estimate -2.299 -.888 .710 1.432 2.806 3.928 -.110 -1.118 .199 0a -.088 -.149 0a


Std. Error .521 .495 .494 .511 .617 .847 .079 .622 .349 . .407 .202 .

Wald 19.436 3.224 2.071 7.841 20.701 21.489 1.938 3.235 .325 . .047 .543 .

df 1 1 1 1 1 1 1 1 1 0 1 1 0

Sig. .000 .073 .150 .005 .000 .000 .164 .072 .568 . .829 .461 .

95% Confidence Interval Lower Bound Upper Bound -3.321 -1.277 -1.858 .081 -.257 1.678 .430 2.435 1.597 4.015 2.267 5.588 -.264 .045 -2.337 .100 -.485 .883 . . -.887 .710 -.544 .247 . .


Table A32 Generational Status and In-built Weaknesses Health Belief Kruskal-Wallis Ranks Generational Status: Of the following definitions for generational status please choose which applies most to you. N Mean Rank I believe I may have First-generation 8 83.31 certain in built immigrant (neither you weaknesses which or your parents were make me vulnerable to Second-generation 24 112.00 particular illness or immigrant (at least one disorders. of your parents wa Third-generation 174 103.26 immigrant or more (both of your parents are Total 206 Table B32. Generational Status and In-built Weaknesses Health Belief Kruskal-Wallis Test Statisticsa,b I believe I may have certain in built weaknesses which make me vulnerable to particular illness or disorders. Chi-Square 1.479 df 2 Asymp. Sig. .477 a. Kruskal Wallis Test b. Grouping Variable: Generational Status: Of the following definitions for generational status please choose which applies most to you.

172


Table A33 Generational Status and Doctor’s Advice Health Belief Kruskal-Wallis Ranks Generational Status: Of the following definitions for generational status please choose which applies most to you. N Mean Rank When I’m ill enough to First-generation 8 57.25 consult a doctor, my immigrant (neither you recovery will be faster or your parents were if I comply properly Second-generation 24 96.65 with the advice and immigrant (at least one treatment I get. of your parents wa Third-generation 174 106.57 immigrant or more (both of your parents are Total 206 Table B33 Generational Status and Doctor’s Advice Health Belief Kruskal-Wallis Test Statisticsa,b When I’m ill enough to consult a doctor, my recovery will be faster if I comply properly with the advice and treatment I get. Chi-Square 6.542 df 2 Asymp. Sig. .038 a. Kruskal Wallis Test b. Grouping Variable: Generational Status: Of the following definitions for generational status please choose which applies most to you.

173

ACCULTURATION AND HEALTH BELIEFS Table A34 Generational Status and Environmental Improvement Health Belief Kruskal-Wallis Ranks Generational Status: Of the following definitions for generational status please choose which applies most to you. N Mean Rank To improve my health First-generation 8 42.25 would require immigrant (neither you improvements in the or your parents were environment in which I Second-generation 24 106.06 live. immigrant (at least one of your parents wa Third-generation 174 105.96 immigrant or more (both of your parents are Total 206 Table B34 Generational Status and Environmental Improvement Health Belief Kruskal-Wallis Test Statisticsa,b To improve my health would require improvements in the environment in which I live. Chi-Square 9.186 df 2 Asymp. Sig. .010 a. Kruskal Wallis Test b. Grouping Variable: Generational Status: Of the following definitions for generational status please choose which applies most to you.

174

ACCULTURATION AND HEALTH BELIEFS Table A35 Generational Status and Surroundings Quality Health Belief KruskalWallis Ranks Generational Status: Of the following definitions for generational status please choose which applies most to you. N Mean Rank When I’m ill, my First-generation 8 64.75 recovery is influenced immigrant (neither you by the quality and or your parents were comfort of my Second-generation 24 100.94 surrounding. immigrant (at least one of your parents wa Third-generation 174 105.64 immigrant or more (both of your parents are Total 206 Table B35 Generational Status and Surroundings Quality Health Belief Kruskal-Wallis Test Statisticsa,b When I’m ill, my recovery is influenced by the quality and comfort of my surrounding. Chi-Square 4.075 df 2 Asymp. Sig. .130 a. Kruskal Wallis Test b. Grouping Variable: Generational Status: Of the following definitions for generational status please choose which applies most to you.

175

ACCULTURATION AND HEALTH BELIEFS Table A36 Generational Status and Medication Health Belief Kruskal-Wallis Ranks Generational Status: Of the following definitions for generational status please choose which applies most to you. N Mean Rank I usually expect to take First-generation 8 61.69 medicine to help me immigrant (neither you recover from illness. or your parents were Second-generation 24 80.94 immigrant (at least one of your parents wa Third-generation 174 108.53 immigrant or more (both of your parents are Total 206 Table B36 Generational Status and Medication Health Belief Kruskal-Wallis Test Statisticsa,b I usually expect to take medicine to help me recover from illness. Chi-Square 8.946 df 2 Asymp. Sig. .011 a. Kruskal Wallis Test b. Grouping Variable: Generational Status: Of the following definitions for generational status please choose which applies most to you.

176

ACCULTURATION AND HEALTH BELIEFS Table A37 Generational Status and Infectious Organisms Health Belief KruskalWallis Ranks Generational Status: Of the following definitions for generational status please choose which applies most to you. N Mean Rank My state of health at First-generation 8 79.38 any time is immigrant (neither you considerably influenced or your parents were by whether or not I’ve Second-generation 24 93.35 been exposed to immigrant (at least one infectious or contagious of your parents wa disease organisms. Third-generation 174 106.01 immigrant or more (both of your parents are Total 206

177

ACCULTURATION AND HEALTH BELIEFS Table B37 Generational Status and Infectious Organisms Health Belief Kruskal-Wallis Test Statisticsa,b My state of health at any time is considerably influenced by whether or not I’ve been exposed to infectious or contagious disease organisms. Chi-Square 2.439 df 2 Asymp. Sig. .295 a. Kruskal Wallis Test b. Grouping Variable: Generational Status: Of the following definitions for generational status please choose which applies most to you.

178

ACCULTURATION AND HEALTH BELIEFS Table A38 Generational Status and God Provides Health Belief Kruskal-Wallis Ranks Generational Status: Of the following definitions for generational status please choose which applies most to you. N Mean Rank God has given me the First-generation 8 87.44 means by which to immigrant (neither you improve my health. or your parents were Second-generation 24 104.38 immigrant (at least one of your parents wa Third-generation 174 104.12 immigrant or more (both of your parents are Total 206 Table B38 Generational Status and God Provides Health Belief Kruskal-Wallis Test Statisticsa,b God has given me the means by which to improve my health. Chi-Square .677 df 2 Asymp. Sig. .713 a. Kruskal Wallis Test b. Grouping Variable: Generational Status: Of the following definitions for generational status please choose which applies most to you.

179

ACCULTURATION AND HEALTH BELIEFS Table A39 Generational Status and Self-care Health Belief Kruskal-Wallis Ranks Generational Status: Of the following definitions for generational status please choose which applies most to you. N Mean Rank How well or badly I First-generation 8 100.00 look after myself immigrant (neither you generally has an or your parents were influence on my overall Second-generation 24 106.40 health. immigrant (at least one of your parents wa Third-generation 174 103.26 immigrant or more (both of your parents are Total 206 Table B39 Generational Status and Self-care Health Belief Kruskal-Wallis Test Statisticsa,b How well or badly I look after myself generally has an influence on my overall health. Chi-Square .105 df 2 Asymp. Sig. .949 a. Kruskal Wallis Test b. Grouping Variable: Generational Status: Of the following definitions for generational status please choose which applies most to you.

180

ACCULTURATION AND HEALTH BELIEFS Table A40 Generational Status and Virulence Health Belief Kruskal-Wallis Ranks Generational Status: Of the following definitions for generational status please choose which applies most to you. N Mean Rank The speed of my First-generation 8 81.88 recovery from an immigrant (neither you infection depends a lot or your parents were on the virulence of the Second-generation 24 116.88 disease organisms immigrant (at least one causing it. of your parents wa Third-generation 174 102.65 immigrant or more (both of your parents are Total 206 Table B40 Generational Status and Virulence Health Belief Kruskal-Wallis Test Statisticsa,b The speed of my recovery from an infection depends a lot on the virulence of the disease organisms causing it. Chi-Square 2.617 df 2 Asymp. Sig. .270 a. Kruskal Wallis Test b. Grouping Variable: Generational Status: Of the following definitions for generational status please choose which applies most to you.

181

ACCULTURATION AND HEALTH BELIEFS Table A41 Generational Status and Illness as a Challenge Health Belief KruskalWallis Ranks Generational Status: Of the following definitions for generational status please choose which applies most to you. N Mean Rank I see illness as a First-generation 8 68.38 challenge to be immigrant (neither you overcome—a or your parents were determined attitude on Second-generation 24 109.73 my part can speed my immigrant (at least one recovery. of your parents wa Third-generation 174 104.26 immigrant or more (both of your parents are Total 206 Table B41 Generational Status and Illness as a Challenge Health Belief Kruskal-Wallis Test Statisticsa,b I see illness as a challenge to be overcome—a determined attitude on my part can speed my recovery. Chi-Square 3.308 df 2 Asymp. Sig. .191 a. Kruskal Wallis Test b. Grouping Variable: Generational Status: Of the following definitions for generational status please choose which applies most to you.

182


183

Table 42 Generational Status and Doctor’s Advice Health Belief Games-Howell Multiple Comparisons Dependent Variable: When I’m ill enough to consult a doctor, my recovery will be faster if I comply properly with the advice and treatment I get. Games-Howell (I) Generational Status: Of the (J) Generational Status: Of the following definitions for

following definitions for

generational status please


choose which applies most to


you.

you.

First-generation immigrant

Second-generation immigrant

(neither you or your parents

(at least one of your parents

were

wa Third-generation immigrant or

95% Confidence Interval

Mean Difference (I-J)

Std. Error

Sig.

Lower Bound

Upper Bound

-.62500

.28387

.090

-1.3315

.0815

-.69397*

.19315

.015

-1.2369

-.1511

.62500

.28387

.090

-.0815

1.3315

-.06897

.22567

.950

-.6287

.4907

.69397*

.19315

.015

.1511

1.2369

.06897

.22567

.950

-.4907

.6287

more (both of your parents are Second-generation immigrant




wa

were Third-generation immigrant or more (both of your parents are

Third-generation immigrant or First-generation immigrant more (both of your parents are (neither you or your parents were Second-generation immigrant (at least one of your parents wa *. The mean difference is significant at the 0.05 level.


184

Table 43. Generational Status and Environmental Improvement Health Belief Games-Howell Multiple Comparisons Dependent Variable: To improve my health would require improvements in the environment in which I live. Games-Howell (I) Generational Status: Of the (J) Generational Status: Of the following definitions for

following definitions for





you.

you.


Second-generation immigrant



were

wa Third-generation immigrant or



Std. Error

Sig.

Lower Bound

Upper Bound

-1.50000*

.37349

.001

-2.4331

-.5669

-1.58908*

.27883

.000

-2.3442

-.8340

1.50000*

.37349

.001

.5669

2.4331

-.08908

.30371

.954

-.8345

.6563

1.58908*

.27883

.000

.8340

2.3442

.08908

.30371

.954

-.6563

.8345

more (both of your parents are Second-generation immigrant




wa

were Third-generation immigrant or more (both of your parents are

Third-generation immigrant or First-generation immigrant more (both of your parents are (neither you or your parents were Second-generation immigrant (at least one of your parents wa *. The mean difference is significant at the 0.05 level.


185

Table 44. Generational Status and Medication Health Belief Games-Howell Multiple Comparisons Dependent Variable: I usually expect to take medicine to help me recover from illness. Games-Howell (I) Generational Status: Of the following definitions for generational status please choose which applies most to you.

(J) Generational Status: Of the following definitions for generational status please choose which applies most to you.

First-generation immigrant Second-generation (neither you or your parents immigrant (at least one of were your parents wa



Std. Error

Sig.

Lower Bound

Upper Bound

-.58333

.55281

.556

-2.0250

.8584

Third-generation immigrant or more (both of your parents are

-1.25000

.46873

.066

-2.5890

.0890

First-generation immigrant (neither you or your parents were

.58333

.55281

.556

-.8584

2.0250

Third-generation immigrant or more (both of your parents are

-.66667

.33852

.137

-1.5012

.1679

Third-generation immigrant First-generation immigrant or more (both of your (neither you or your parents parents are were

1.25000

.46873

.066

-.0890

2.5890

.66667

.33852

.137

-.1679

1.5012

Second-generation immigrant (at least one of your parents wa

Second-generation immigrant (at least one of your parents wa

ACCULTURATION AND HEALTH BELIEFS Table 45 MA and HA Descriptive Statistics Mainstream Heritage Acculturation Acculturation Score Score N Valid 206 206 Missing 0 0 Mean 6.6689 5.9811 Median 6.9000 6.0000 Std. Deviation 1.55457 1.56787 Variance 2.417 2.458 Skewness -1.016 -.509 Std. Error of Skewness .169 .169 Kurtosis 1.324 .559 Std. Error of Kurtosis .337 .337

186

ACCULTURATION AND HEALTH BELIEFS Table 46 Generational Status, MA, and HA Kruskal-Wallis Ranks Generational Status: Of the following definitions for generational status please choose which applies most to you. N Mean Rank Mainstream First-generation 8 113.00 Acculturation Score immigrant (neither you or your parents were Second-generation 24 116.50 immigrant (at least one of your parents wa Third-generation 174 101.27 immigrant or more (both of your parents are Total 206 Heritage Acculturation First-generation 8 110.94 Score immigrant (neither you or your parents were Second-generation 24 116.04 immigrant (at least one of your parents wa Third-generation 174 101.43 immigrant or more (both of your parents are Total 206

187

ACCULTURATION AND HEALTH BELIEFS Table 47 Generational Status, MA, and HA KruskalWallis Test Statisticsa Mainstream Heritage Acculturation Acculturation Score Score N 206 206 Median 6.9000 6.0000 b Chi-Square 2.155 .957c df 2 2 Asymp. Sig. .341 .620 a. Grouping Variable: Generational Status: Of the following definitions for generational status please choose which applies most to you. b. 2 cells (33.3%) have expected frequencies less than 5. The minimum expected cell frequency is 3.9. c. 2 cells (33.3%) have expected frequencies less than 5. The minimum expected cell frequency is 3.9.

188


189

References Abu-Rayya, H. M. (2007). Acculturation, Christian religiosity, and psychological and marital well-being among the European wives of Arabs in Israel. Mental Health, Religion & Culture, 10(2), 171-190. doi: 10.1080/13694670500504901 Ahlqvist, M., & Wirfält, E. (2000). Beliefs concerning dietary practices during pregnancy and lactation. Scandinavian Journal of Caring Sciences, 14(2), 105-111. doi: 10.1111/j.1471-6712.2000.tb00570.x Alba, R., & Nee, V. (1997). Rethinking assimilation theory for a new era of immigration. International Migration Review, 826-874. doi: 10.2307/2547416 Amador, J. A., Flynn, P. M., & Betancourt, H. (2015). Cultural beliefs about health professionals and perceived empathy influence continuity of cancer screening following a negative encounter. Journal of Behavioral Medicine, 38(5), 798-808. doi: 10.1007/s10865-015-9646-1 An, N., Cochran, S. D., Mays, V. M., & McCarthy, W. J. (2008). Influence of American acculturation on cigarette smoking behaviors among Asian American subpopulations in California. Nicotine & Tobacco Research, 10(4), 579-587. doi: 10.1080/14622200801979126 Anderson, L. M., Wood, D. L., & Sherbourne, C. D. (1997). Maternal acculturation and childhood immunization levels among children in Latino families in Los Angeles. American Journal of Public Health, 87(12), 2018-2021. doi: 10.2105/AJPH.87.12.2018


190

Andridge, R. R., & Little, R. J. A. (2010). A review of hot deck imputation for survey non-response. International Statistical Review, 78(1), 40–64. doi: 10.1111/j.17515823.2010.00103.x Arbona, C., Olvera, N., Rodriguez, N., Hagan, J., Linares, A., & Wiesner, M. (2010). Acculturative stress among documented and undocumented Latino immigrants in the United States. Hispanic Journal of Behavioral Sciences, 32(3), 362–384. doi: 10.1177/0739986310373210 Årestedt, L., Benzein, E., & Persson, C. (2015). Families living with chronic illness: Beliefs about illness, family, and health care. Journal of Family Nursing, 21(2), 206-231. doi:10.1177/1074840715576794 Asare, M., & Sharma, M. (2012). Role of Health Belief Model on sexual communication among African immigrants. American Journal of Health Studies, 27(2), 97-106. Retrieved from: http://web.a.ebscohost.com/abstract?direct=true&profile=ehost&scope=site&auth type=crawler&jrnl=10900500&AN=83866345&h=r0pirLF6oDeuRYgNZFPlsx6 %2b%2bL54eJ0dSr6bD2lEelfrI8K9xXdNUfTnjWfyZABOOm1%2fPWun1nVpa nYj6eDjpg%3d%3d&crl=c&resultNs=AdminWebAuth&resultLocal=ErrCrlNotA uth&crlhashurl=login.aspx%3fdirect%3dtrue%26profile%3dehost%26scope%3ds ite%26authtype%3dcrawler%26jrnl%3d10900500%26AN%3d83866345 Austin, L. T., Ahmad, F., McNally, M. J., & Stewart, D. E. (2002). Breast and cervical cancer screening in Hispanic women: a literature review using the health belief model. Women's Health Issues, 12(3), 122-128. doi: 10.1016/S10493867(02)00132-9


191

Baek Choi, J., & Thomas, M. (2009). Predictive factors of acculturation attitudes and social support among Asian immigrants in the USA. International Journal of Social Welfare, 18(1), 76-84. Doi: 10.1111/j.1468-2397.2008.00567.x Balcazar, H., Castro, F. G., & Krull, J. L. (1995). Cancer risk reduction in Mexican American women: the role of acculturation, education, and health risk factors. Health Education & Behavior, 22(1), 61-84. doi: 10.1177/109019819502200107 Baldwin, A. S., Bruce, C. M., & Tiro, J. A. (2012). Understanding how mothers of adolescent girls obtain information about the human papillomavirus vaccine: Associations between mothers' health beliefs, information seeking, and vaccination intentions in an ethnically diverse sample. Journal of Health Psychology, 18(7), 926-938. doi: 10.1177/1359105312445078 Begley, K., Mclaws, M., Ross, M. W., & Gold, J. (2008). Cognitive and behavioural correlates of non-adherence to HIV anti-retroviral therapy: Theoretical and practical insight for clinical psychology and health psychology. Clinical Psychologist, 12(1), 9-17. doi: 10.1080/13284200802069043 Bereolos, N. M. (2007). The role of acculturation in the health belief model for MexicanAmericans with type II diabetes (Doctoral dissertation, University of North Texas). Retrieved from: http://digital.library.unt.edu/ark:/67531/metadc4001/m2/1/high_res_d/dissertation .pdf Berry, J. W. (1990). Psychology of acculturation. In R. W. Brislin (Ed.), Applied Crosscultural Psychology (pp. 232-253). Newbury Park, CA: Sage.


192

Berry, J. W. (2005). Acculturation: Living successfully in two cultures. International Journal of Intercultural Relations, 29(6), 697-712. Bischof, G. H., Mohr, L. A., & Lieser, M. L. (2004). Family therapy meets internal medicine: Innovations in medical family therapy. Michigan Family Review, 9(1). Retrieved from: http://quod.lib.umich.edu/m/mfr/4919087.0009.103/--familytherapy-meets-internal-medicine-innovations?rgn=main;view=fulltext Bohlmeijer, E., Prenger, R., Taal, E., & Cuijpers, P. (2010). The effects of mindfulnessbased stress reduction therapy on mental health of adults with a chronic medical disease: a meta-analysis. Journal of Psychosomatic Research, 68(6), 539-544. doi: 10.1016/j.jpsychores.2009.10.005 Borowski, S. C., & Tambling, R. B. (2015). Applying the Health Belief Model to young individuals’ beliefs and preferences about premarital counseling. The Family Journal, 23(4), 417-426. doi:10.1177/1066480715602221 Borrell-Carrió, F., Suchman, A. L., & Epstein, R. M. (2004). The biopsychosocial model 25 years later: Principles, practice, and scientific inquiry. Annals of Family Medicine, 2(6), 576–582. http://doi.org/10.1370/afm.245 Bourhis, R. Y., Moise, L. C., Perreault, S., & Senecal, S. (1997). Towards an interactive acculturation model: A social psychological approach. International Journal of Psychology, 32(6), 369-386. doi: 10.1080/002075997400629 Brown, C. G., Patrician, P. A., & Brosch, L. R. (2012). Increasing testicular selfexamination in active duty soldiers: An intervention study. Medsurg Nursing, 21(2), 97. PMID: 22667002


193

Buglar, M. E., White, K. M., & Robinson, N. G. (2010). The role of self-efficacy in dental patients’ brushing and flossing: testing an extended Health Belief Model. Patient Education and Counseling, 78(2), 269-272. doi: 10.1016/j.pec.2009.06.014 Burr, B. K., Kuns, J. B., Atkins, L., Bertram, A. G., & Sears, K. (2015). Generation to generation: Passing on religious and spiritual beliefs and practices. Journal of Religion, Spirituality & Aging, 27(2-3), 183-200. doi: 10.1080/15528030.2015.1007544 Buus, N., Jensen, L. D., Maribo, T., Gonge, B. K., & Angel, S. (2014). Low back pain patients' beliefs about effective/ineffective constituents of a counseling intervention: a follow-up interview study. Disability & Rehabilitation, 37(11), 936-941. doi:10.3109/09638288.2014.948135 Byrd, T. L., Peterson, S. K., Chavez, R., & Heckert, A. (2004). Cervical cancer screening beliefs among young Hispanic women. Preventive Medicine, 38(2), 192-197. doi: 10.1016/j.ypmed.2003.09.017 Calnan, M., & Rutter, D. R. (1986). Do health beliefs predict health behaviour? An analysis of breast self-examination. Social Science & Medicine, 22(6), 673-678. doi: 10.1016/0277-9536(86)90039-0 Campbell, T. L. (2003). The effectiveness of family interventions for physical disorders. Journal of Marital and Family Therapy, 29(2), 263-281. doi: 10.1111/j.1752-0606.2003.tb01204.x Campinha-Bacote, J. (2003). Many faces: Addressing diversity in health care. Online Journal of Issues in Nursing, 8(1), 3. Retrieved from:


194

http://www.nursingworld.org/MainMenuCategories/ANAMarketplace/ANAPerio dicals/OJIN/TableofContents/Volume82003/No1Jan2003/AddressingDiversityin HealthCare.aspxThank Caplan, S., Escobar, J., Paris, M., Alvidrez, J., Dixon, J. K., Desai, M. M., ... & Whittemore, R. (2013). Cultural influences on causal beliefs about depression among Latino immigrants. Journal of Transcultural Nursing, 24(1), 68-77. doi: 10.1177/1043659612453745 Carpenter, C. J. (2010). A meta-analysis of the effectiveness of health belief model variables in predicting behavior. Health Communication, 25(8), 661-669. doi: 10.1080/10410236.2010.521906 Carroll, J., Epstein, R., Fiscella, K., Volpe, E., Diaz, K., & Omar, S. (2007). Knowledge and beliefs about health promotion and preventive health care among Somali Women in the United States. Health Care for Women International, 28(4), 360380. doi: 10.1080/07399330601179935 Cartmill, M. (1998). The status of the race concept in physical anthropology. American Anthropologist, 100(3), 651-660. doi: 10.1525/aa.1998.100.3.651 Castro, F. G., Furth, P., & Karlow, H. (1984). The health beliefs of Mexican, Mexican American and Anglo American women. Hispanic Journal of Behavioral Sciences, 6(4), 365-383. doi: 10.1177/07399863840064003 Chen, J. Y., Fox, S. A., Cantrell, C. H., Stockdale, S. E., & Kagawa-Singer, M. (2006). Health disparities and prevention: Racial/ethnic barriers to flu vaccinations. Journal of Community Health, 32(1), 5-20. doi: 10.1007/s10900-006-9031-7


195

Chen, C. J., Kendall, J., & Shyu, Y. I. L. (2010). Grabbing the rice straw: health information seeking in Chinese immigrants in the United States. Clinical Nursing Research, 19(4), 335-353. doi: 10.1177/1054773810372542 Chen, H., Tu, S. P., Teh, C. Z., Yip, M. P., Choe, J. H., Hislop, T. G., ... & Thompson, B. (2006). Lay beliefs about hepatitis among North American Chinese: implications for hepatitis prevention. Journal of Community Health, 31(2), 94-112. doi: 10.1007/s10900-005-9000-6 Chen, L., Varghese, F., Nurcombe, B., & Kenardy, J. (1999). Ethnic identity, acculturation and mental health in young Chinese immigrants, presented at the International Conference: Culture, Race and Community: Making it Work in the New Millennium, Melbourne, 1999. Cho, H.-W., & Chu, C. (2012). Can stigma still distort the spectrum of a disease? Osong Public Health and Research Perspectives, 3(2), 65–67. doi: 10.1016/j.phrp.2012.04.009 Cohen, A. B., & Koenig, H. G. (2003). Religion, religiosity and spirituality in the biopsychosocial model of health and ageing. Ageing International, 28(3), 215241. doi: 10.1007/s12126-002-1005-1 Cole, E., Stevenson, M., & Rodgers, B. (2009). The influence of cultural health beliefs on self-reported mental health status and mental health service utilization in an ethnically diverse sample of older adults. Journal of Feminist Family Therapy, 21(1), 1-17. doi: 10.1080/08952830802683657 Crawford, J., Ahmad, F., Beaton, D., & Bierman, A. S. (2015). Cancer screening behaviours among South Asian immigrants in the UK, US and Canada: A scoping


196

study. Health & Social Care in the Community, 24(2), 123-153. doi: 10.1111/hsc.12208 Dale, W., Sartor, O., Davis, T., & Bennett, C. L. (1999). Understanding barriers to the early detection of prostate cancer among men of lower socioeconomic status. The Prostate Journal, 1(4), 179-184. doi: 10.1046/j.1525-1411.1999.14005.x De Los Monteros, K. E., & Gallo, L. C. (2011). The relevance of fatalism in the study of Latinas’ cancer screening behavior: A systematic review of the literature. International Journal of Behavioral Medicine, 18(4), 310-318. doi: 10.1007/s12529-010-9119-4 Deeks, A., Lombard, C., Michelmore, J., & Teede, H. (2009). The effects of gender and age on health related behaviors. BMC Public Health, 9(1), 213. doi: 10.1186/1471-2458-9-213 Delavari, M., Sønderlund, A. L., Mellor, D., Mohebbi, M., & Swinburn, B. (2015). Migration, acculturation and environment: determinants of obesity among Iranian migrants in Australia. International Journal of Environmental Research and Public Health, 12(2), 1083-1098. doi: 10.3390/ijerph120201083 Doherty, W. J., McDaniel, S. H., & Hepworth, J. (1994). Medical family therapy: An emerging arena for family therapy. Journal of Family Therapy, 16(1), 31-46. doi: 10.1111/j.1467-6427.1994.00775.x Dominelli, L. (2002). Anti-oppressive social work theory and practice. New York: Palgrave MacMillan.


197

Dubé, E., Laberge, C., Guay, M., Bramadat, P., Roy, R., & Bettinger, J. A. (2013). Vaccine hesitancy: An overview. Human Vaccines & Immunotherapeutics, 9(8), 1763–1773. doi: 10.4161/hv.24657 Edelman, D., Christian, A., & Mosca, L. (2009). Association of acculturation status with beliefs, barriers, and perceptions related to cardiovascular disease prevention among racial and ethnic minorities. Journal of Transcultural Nursing, 20(3), 278285. doi: 10.1177/1043659609334852 Elder, J. P., Woodruff, S. I., Candelaria, J., Golbeck, A. L., Alvarez, J. L., Criqui, M. H., ... & Rupp, J. W. (1998). Socioeconomic indicators related to cardiovascular disease risk factors in Hispanics. American Journal of Health Behavior, 22(3), 172-185. Retrieved from: http://web.b.ebscohost.com.ezproxy.simpsonulibrary.org/ehost/detail/detail?vid=6 &sid=4edb4d40-776a-4e05-964c087667006834%40sessionmgr120&hid=116&bdata=JnNpdGU9ZWhvc3QtbGl2 ZQ%3d%3d#AN=107223634&db=c8h Falke, S. I., & D'Arrigo-Patrick, E. (2014). Medical family therapists in action: Embracing multiple roles. Journal of Marital & Family Therapy, 41(4), 428-442. Retrieved from: http://web.a.ebscohost.com.ezproxy.simpsonulibrary.org/ehost/pdfviewer/pdfview er?sid=7458987a-d930-42e1-96f624788da56776%40sessionmgr4009&vid=6&hid=4002 Fayanju, O. M., Kraenzle, S., Drake, B. F., Oka, M., & Goodman, M. S. (2014). Perceived barriers to mammography among underserved women in a breast health


198

center outreach program. American Journal of Surgery, 208(3), 425–434. doi: 10.1016/j.amjsurg.2014.03.005 Finfgeld, D. L., Wongvatunyu, S., Conn, V. S., Grando, V. T., & Russell, C. L. (2003). Health belief model and reversal theory: A comparative analysis. Journal of Advanced Nursing, 43(3), 288-297. doi: 10.1046/j.1365-2648.2003.02712.x Fitzgerald, A., Heary, C., Kelly, C., Nixon, E., & Shevlin, M. (2013). Self-efficacy for healthy eating and peer support for unhealthy eating are associated with adolescents’ food intake patterns. Appetite, 63, 48-58. doi: 10.1016/j.appet.2012.12.011 Franklin, M. D., Schlundt, D. G., & Wallston, K. A. (2008). Development and validation of a religious health fatalism measure for the African-American faith community. Journal of Health Psychology, 13(3), 323-335. doi: 10.1177/1359105307088137 Franzini, L., Ribble, J. C., & Keddie, A. M. (2001). Understanding the Hispanic Paradox. Ethnicity & Disease, 11(3), 496-518. Retrieved from: https://www.researchgate.net/profile/Arlene_Keddie/publication/11776689_Unde rstanding_the_Hispanic_Health_Paradox/links/545145040cf2bf864cba8fb6/Unde rstanding-the-Hispanic-Health-Paradox.pdf Fulton, J. P., Rakowski, W., & Jones, A. C. (1995). Determinants of breast cancer screening among inner-city Hispanic women in comparison with other inner-city women. Public Health Reports, 110(4), 476. Retrieved from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1382158/pdf/pubhealthrep000530102.pdf


199

Furnham, A. (1994). Explaining health and illness: lay beliefs on the nature of health. Personality and Individual Differences, 17(4), 455-466. doi: 10.1016/01918869(94)90083-3 Furnham, A. (1994b). Health Beliefs Questionnaires [Database record]. Retrieved from PsycTESTS. doi: 10.1037/t18863-000 Garcés-Palacio, I. C., & Scarinci, I. C. (2012). Factors associated with perceived susceptibility to cervical cancer among Latina immigrants in Alabama. Maternal and Child Health Journal, 16(1), 242-248. doi: 10.1007/s10995-010-0737-x Gipson, P., & King, C. (2012). Health behavior theories and research: Implications for suicidal individuals’ treatment linkage and adherence. Cognitive and Behavioral Practice, 19(2), 209–217. doi: 10.1016/j.cbpra.2010.11.005 Goforth, A. N., Oka, E. R., Leong, F. T., & Denis, D. J. (2014). Acculturation, acculturative stress, religiosity and psychological adjustment among Muslim Arab American adolescents. Journal of Muslim Mental Health, 8(2). Retrieved from: http://quod.lib.umich.edu/j/jmmh/10381607.0008.202/--acculturationacculturative-stress-religiosity?rgn=main;view=fulltext Gonzalez, P., Lim, J., Wang-Letzkus, M., Flores, K. F., Allen, K. M., Castaneda, S. F., & Talavera, G. A. (2014). Breast cancer cause beliefs: Chinese, Korean, and Mexican American breast cancer survivors. Western Journal of Nursing Research, 37(8), 1081-1099. doi: 10.1177/0193945914541518 Guilfoyle, S., Franco, R., & Gorin, S. S. (2007). Exploring older women's approaches to cervical cancer screening. Health Care for Women International, 28(10), 930950. doi: 10.1080/07399330701615358


200

Harris, P., Loveman, E., Clegg, A., Easton, S., & Berry, N. (2015). Systematic review of cognitive behavioural therapy for the management of headaches and migraines in adults. British Journal of Pain, 9(4), 213–224. doi: 10.1177/2049463715578291 Hendriks, A. M., Gubbels, J. S., Jansen, M. W. J., & Kremers, S. P. J. (2012). Health beliefs regarding dietary behavior and physical activity of Surinamese immigrants of Indian descent in The Netherlands: A qualitative study. ISRN obesity, 2012. doi: 10.5402/2012/903868 Ho, V., Yamal, J. M., Atkinson, E. N., Basen-Engquist, K., Tortolero-Luna, G., & Follen, M. (2005). Predictors of breast and cervical screening in Vietnamese women in Harris County, Houston, Texas. Cancer Nursing, 28(2), 119-129. Retrieved from: https://www.researchgate.net/profile/Edward_Atkinson/publication/7921728_Pre dictors_of_Breast_and_Cervical_Screening_in_Vietnamese_Women_in_Harris_ County_Houston_Texas/links/0fcfd508061717982f000000.pdf Hollier, L. P., Pettigrew, S., Slevin, T., Strickland, M., & Minto, C. (2016). Comparing online and telephone survey results in the context of a skin cancer prevention campaign evaluation. Journal of Public Health, 39(1), 193-201. doi: 10.1093/pubmed/fdw018 Horevitz, E., & Organista, K. C. (2013). The Mexican health paradox: Expanding the explanatory power of the acculturation construct. Hispanic Journal of Behavioral Sciences, 35(1), 3-34. Retrieved from: https://www.researchgate.net/profile/Elizabeth_Horevitz/publication/256422537_ The_Mexican_Health_Paradox_Expanding_the_Explanatory_Power_of_the_Acc ulturation_Construct/links/02e7e5227c2e83f492000000.pdf


201

Im, E. O., & Chee, W. (2005). A descriptive internet survey on menopausal symptoms: five ethnic groups of Asian American university faculty and staff. Journal of Transcultural Nursing, 16(2), 126-135. doi: 10.1177/1043659604273550 Iqbal, S., Oraka, E., Chew, G. L., & Flanders, W. D. (2014). Association between birthplace and current asthma: The role of environment and acculturation. American Journal of Public Health, 104(S1), S175-82. doi:10.2105/AJPH.2013.301509 Janz, N. K., & Becker, M. H. (1984). The health belief model: A decade later. Health Education & Behavior, 11(1), 1-47. doi: 10.1177/109019818401100101 Jemmott, L. S., Maula, E. C., & Bush, E. (1999). Hearing our voices: Assessing HIV prevention needs among Asian and Pacific Islander women. Journal of Transcultural Nursing, 10(2), 102-111. doi: 10.1177/104365969901000203 Jones, A., Cohen, D., Johnstone, B., Yoon, D. P., Schopp, L. H., McCormack, G., & Campbell, J. (2015). Relationships between negative spiritual beliefs and health outcomes for individuals with heterogeneous medical conditions. Journal of Spirituality in Mental Health, 17(2), 135-152. doi: 10.1080/19349637.2015.1023679 Jones, C. L., Jensen, J. D., Scherr, C. L., Brown, N. R., Christy, K., & Weaver, J. (2015). The health belief model as an explanatory framework in communication research: Exploring parallel, serial, and moderated mediation. Health Communication, 30(6), 566–576. doi: 10.1080/10410236.2013.873363


202

Kadden, R. M., & Litt, M. D. (2011). The role of self-efficacy in the treatment of substance use disorders. Addictive Behaviors, 36(12), 1120–1126. doi: 10.1016/j.addbeh.2011.07.032 Kähkönen, O., Kankkunen, P., Saaranen, T., Miettinen, H., Kyngäs, H., & Lamidi, M. L. (2015). Motivation is a crucial factor for adherence to a healthy lifestyle among people with coronary heart disease after percutaneous coronary intervention. Journal of Advanced Nursing, 71(10), 2364-2373. doi: 10.1111/jan.12708 Kawakami, A., Tanaka, M., Nishigaki, M., Yoshimura, N., Suzuki, R., Maeda, S., . . . Yamamoto-Mitani, N. (2014). A screening instrument to identify ulcerative colitis patients with the high possibility of current non-adherence to aminosalicylate medication based on the Health Belief Model: A cross-sectional study. BMC Gastroenterology, 14(1). doi: 10.1186/s12876-014-0220-z Kissane, D. W., Grabsch, B., Clarke, D. M., Christie, G., Clifton, D., Gold, S., & ... Smith, G. C. (2004). Supportive-expressive group therapy: The transformation of existential ambivalence into creative living while enhancing adherence to anticancer therapies. Psycho-Oncology, 13(11), 755-768. doi:10.1002/pon.798 Kluger, A. N., & Tikochinsky, J. (2001). The error of accepting the “theoretical" null hypothesis: The rise, fall, and resurrection of commonsense hypotheses in psychology. Psychological Bulletin, 127(3), 408. PMID: 11393303 Kowal, J., Johnson, S. M., & Lee, A. (2003). Chronic illness in couples: A case for emotionally focused therapy. Journal of Marital and Family Therapy, 29(3), 299310. doi: 10.1111/j.1752-0606.2003.tb01208.x


203

Kratzke, C., Amatya, A., & Vilchis, H. (2013). Differences among college women for breast cancer prevention acquired information-seeking, desired apps and texts, and daughter-initiated information to mothers. Journal of Community Health, 39(2), 291-300. doi: 10.1007/s10900-013-9759-9 Lee, J., Jezewski, M. A., Wu, Y. B., & Carvallo, M. (2011). The relationship between acculturation and oral contraceptive use among Korean immigrant women. Research in Nursing & Health, 34(2), 91-102. doi:10.1002/nur.20417 Lee, H., Kim, J., & Han, H. (2009). Do cultural factors predict mammography behavior among Korean immigrants in the USA? Journal of Advanced Nursing, 65(12), 2574-2584. doi: 10.1111/j.1365-2648.2009.05155.x Lee, S. E., Lee, H. Y., & Diwan, S. (2009). What do Korean American immigrants know about Alzheimer's disease (AD)? The impact of acculturation and exposure to the disease on AD knowledge. International Journal of Geriatric Psychiatry, 25(1), 66-73. doi: 10.1002/gps.2299 Lee, H. Y., Stange, M. J., & Ahluwalia, J. S. (2014). Breast cancer screening behaviors among Korean American immigrant women: Findings from the Health Belief Model. Journal of Transcultural Nursing, 26(5), 450-457. doi: 10.1177/1043659614526457 Lee, H. Y., & Vang, S. (2010). Barriers to cancer screening in Hmong Americans: The influence of health care accessibility, culture, and cancer literacy. Journal of Community Health, 35(3), 302-314. doi: 10.1007/s10900-010-9228-7 Lee-Lin, F., Menon, U., Pett, M., Nail, L., Lee, S., & Mooney, K. (2008). Measuring breast cancer and mammography screening beliefs among Chinese American


204

immigrants. Western Journal of Nursing Research, 30(7), 852-868. doi: 10.1177/0193945908319247 Levitt, H. M., Pomerville, A., & Surace, F. I. (2016). A qualitative meta-analysis examining clients' experiences of psychotherapy: A new agenda. Psychological Bulletin, 142(8), 801. doi: 10.1037/bul0000057 Li, W., Stotts, N. A., & Froelicher, E. S. (2007). Compliance with antihypertensive medication in Chinese immigrants: Cultural specific issues and theoretical application. Research and Theory for Nursing Practice, 21(4), 236-254. PMID: 18236769 Liang, W., Yuan, E., Mandelblatt, J. S., & Pasick, R. J. (2004). How do older Chinese women view health and cancer screening? Results from focus groups and implications for interventions. Ethnicity & Health, 9(3), 283-304. doi: 10.1080/1355785042000250111 Lin, S. Y. (2012). Beliefs about causes, symptoms, and stigma associated with severe mental illness among ‘highly acculturated’ Chinese-American patients. International Journal of Social Psychiatry, 59(8), 745-751. doi: 10.1177/0020764012454384 Lin, P., Simoni, J. M., & Zemon, V. (2005). The health belief model, sexual behaviors, and HIV risk among Taiwanese immigrants. AIDS Education & Prevention, 17(5), 469-483. doi: 10.1521/aeap.2005.17.5.469 Livneh, H., & Antonak, R. F. (2005). Psychosocial adaptation to chronic illness and disability: A primer for counselors. Journal of Counseling & Development, 83(1), 12-20. doi: 10.1002/j.1556-6678.2005.tb00575.x


205

Lopez-Class, M., Castro, F. G., & Ramirez, A. G. (2011). Conceptions of acculturation: A review and statement of critical issues. Social Science & Medicine, 72(9), 1555-1562. doi: 10.1016/j.socscimed.2011.03.011 Lutsey, P. L., Diez Roux, A. V., Jacobs Jr, D. R., Burke, G. L., Harman, J., Shea, S., & Folsom, A. R. (2008). Associations of acculturation and socioeconomic status with subclinical cardiovascular disease in the multi-ethnic study of atherosclerosis. American Journal of Public Health, 98(11), 1963-1970. doi: 10.2105/AJPH.2007.123844 Ma, G. X., Gao, W., Fang, C. Y., Tan, Y., Feng, Z., Ge, S., & Nguyen, J. A. (2013). Health beliefs associated with cervical cancer screening among Vietnamese Americans. Journal of Women's Health, 22(3), 276-288. doi:10.1089/jwh.2012.3587 Macintyre, S., McKay, L., & Ellaway, A. (2006). Lay concepts of the relative importance of different influences on health; are there major socio-demographic variations? Health Education Research, 21(5), 731-739. doi: 10.1093/her/cyl015 Maciocia, G. (2015). The foundations of Chinese medicine: A comprehensive text. London, England: Elsevier Health Sciences. Marshall, T., Jones, D. P., Ramchandani, P. G., Stein, A., & Bass, C. (2007). Intergenerational transmission of health beliefs in somatoform disorders. The British Journal of Psychiatry, 191(5), 449-450. doi: 10.1192/bjp.bp.107.035261 Martinez, O., Wu, E., Shultz, A. Z., Capote, J., Rios, J. L., Sandfort, T., ... & Moya, E. (2014). Still a hard-to-reach population? Using social media to recruit Latino gay


206

couples for an HIV intervention adaptation study. Journal of Medical Internet Research, 16(4), e113. doi: 10.2196/jmir.3311 Massey, D. S. (1995). The new immigration and ethnicity in the United States. Population and Development Review, 21(3), 631-652. doi: 10.2307/2137753 Mcgarvey, E. L., Clavet, G. J., Li, J. B., Butler, A., Cook, K. O., & Pennino, B. (2003). Cancer screening practices and attitudes: Comparison of low-income women in three ethnic groups. Ethnicity & Health, 8(1), 71-82. doi: 10.1080/13557850303556 Meana, M., Bunston, T., George, U., Wells, L., & Rosser, W. (2001). Influences on breast cancer screening behaviors in Tamil immigrant women 50 years old and over. Ethnicity & Health, 6(3-4), 179-188. doi: 10.1080/13557850120078107 Meer, S., & Mir, G. (2014). Muslims and depression: The role of religious beliefs in therapy. Journal of Integrative Psychology and Therapeutics, 2(1), 2. Retrieved from: http://www.hoajonline.com/psychology/2054-4723/2/2 Mehta, R., & Suvadas, E. (1995). Comparing response rates and response content in mail versus electronic mail surveys. Journal of the Market Research Society, 37(4), 429–439. Retrieved from: http://web.b.ebscohost.com.ezproxy.simpsonulibrary.org/ehost/detail/detail?vid=3 &sid=10858d7f-20aa-4799-bae16d65e1a3f3fb%40sessionmgr103&hid=116&bdata=JnNpdGU9ZWhvc3QtbGl2Z Q%3d%3d#AN=1996-13166-001&db=psyh


207

Meillier, L. K., Lund, A. B., & Kok, G. (1997). Cues to action in the process of changing lifestyle. Patient Education and Counseling, 30(1), 37-51. Retrieved from: http://s3.amazonaws.com/academia.edu.documents/45733850/Cues_to_action_in _the_process_of_chaning20160517-695515d1yab.pdf?AWSAccessKeyId=AKIAIWOWYYGZ2Y53UL3A&Expires=1487 047016&Signature=Kr9t6qo1qM0VIITLdx3Lp6yGd%2FE%3D&responsecontentdisposition=inline%3B%20filename%3DCues_to_action_in_the_process_of_cha ning.pdf Mosby. (2009). Health beliefs. Mosby's Medical Dictionary, 8th edition. (2009). Retrieved November 1, 2016 from http://medicaldictionary.thefreedictionary.com/health+beliefs Moss-Morris, R., Weinman, J., Petrie, K., Horne, R., Cameron, L., & Buick, D. (2002). The revised illness perception questionnaire (IPQ-R). Psychology and Health, 17(1), 1-16. doi: 10.1080/08870440290001494 Mudd-Martin, G., Biddle, M. J., Chung, M. L., Lennie, T. A., Bailey, A. L., Casey, B. R., ... & Moser, D. K. (2014). Rural Appalachian perspectives on heart health: Social ecological contexts. American Journal of Health Behavior, 38(1), 134-143. doi: 10.5993/AJHB.38.1.14 Ndukwe, E. G., Williams, K. P., & Sheppard, V. (2013). Knowledge and perspectives of breast and cervical cancer screening among female African immigrants in the Washington D.C. metropolitan area. Journal of Cancer Education, 28(4), 748754. doi: 10.1007/s13187-013-0521-x


208

Negy, C., Hammons, M. E., Reig-Ferrer, A., & Carper, T. M. (2010). The importance of addressing acculturative stress in marital therapy with Hispanic immigrant women. International Journal of Clinical and Health Psychology, 10(1), 5-21. Retrieved from: http://web.a.ebscohost.com.ezproxy.simpsonulibrary.org/ehost/pdfviewer/pdfview er?vid=37&sid=d6e64800-ee0d-4f9c-ab58298c9260d815%40sessionmgr4010&hid=4207 Nguyen, G. T., Barg, F. K., Armstrong, K., Holmes, J. H., & Hornik, R. C. (2008). Cancer and communication in the health care setting: Experiences of older Vietnamese immigrants, a qualitative study. Journal of General Internal Medicine, 23(1), 45-50. doi: 10.1007/s11606-007-0455-2 Ngo, V. H. (2008). A critical examination of acculturation theories. Critical Social Work, 9(1), 1-6. Retrieved from: http://www1.uwindsor.ca/criticalsocialwork/a-criticalexamination-of-acculturation-theories Niederdeppe, J., & Levy, A. G. (2007). Fatalistic beliefs about cancer prevention and three prevention behaviors. Cancer Epidemiology Biomarkers & Prevention, 16(5), 998-1003. doi: 10.1158/1055-9965.EPI-06-0608 O'Connor, P. J., Martin, B., Weeks, C. S., & Ong, L. (2014). Factors that influence young people's mental health help-seeking behaviour: A study based on the Health Belief Model. Journal of Advanced Nursing, 70(11), 2577-2587. doi:10.1111/jan.12423


209

Orji, R., Vassileva, J., & Mandryk, R. (2012). Towards an effective health interventions design: An extension of the health belief model. Online Journal of Public Health Informatics, 4(3). doi: 10.5210/ojphi.v4i3.4321 Ohsawa, G. (1973). The unique principle. George Ohsawa Macrobiotic. Outcomes planning and evaluation unit. (2014). Demographics. Shasta County Health and Human Services, Retrieved from: http://www.co.shasta.ca.us/docs/HHSA/Community-HealthData/demographics2014.pdf?sfvrsn=0 Outcomes planning and evaluation unit. (2014). Demographics draft. Shasta County Health and Human Services, Retrieved from: http://www.co.shasta.ca.us/docs/HHSA/about-us/chsa-draft040816.pdf?sfvrsn=44 Oxford University Press. Ethnocultural. (n.d.). Retrieved November 1, 2016, from https://en.oxforddictionaries.com/definition/ethnocultural Padela, A. I., Murrar, S., Adviento, B., Liao, C., Hosseinian, Z., Peek, M., & Curlin, F. (2015). Associations between religion-related factors and breast cancer screening among American Muslims. Journal of Immigrant and Minority Health / Center for Minority Public Health, 17(3), 660–669. doi: 10.1007/s10903-014-0014-y Palmquist, A. E. L., Wilkinson, A. V., Sandoval, J.-M., & Koehly, L. M. (2012). Agerelated differences in biomedical and folk beliefs as causes for diabetes and heart disease among Mexican origin adults. Journal of Immigrant and Minority Health / Center for Minority Public Health, 14(4), 596–601. doi: 10.1007/s10903-0119522-1


210

Paulhus, D. L., & Vazire, S. (2007). The self-report method. In R. W. Robins, R. C. Fraley, & R. F. Krueger (Eds.), Handbook of Research Methods in Personality Psychology (pp. 224-239). New York, NY: The Guilford Press. Pavlish, C. L., Noor, S., & Brandt, J. (2010). Somali immigrant women and the American health care system: Discordant beliefs, divergent expectations, and silent worries. Social Science & Medicine, 71(2), 353-361. doi: 10.1016/j.socscimed.2010.04.010 Peete, C. T. (1999). The importance of place of residence in health outcomes research: How does living in an ethnic enclave affect low birthweight deliveries for Hispanic mothers? (Doctoral dissertation, University of California, Berkeley, Spring 1999.). Pérez-Stable, E. J., Ramirez, A., Villareal, R., Talavera, G. A., Trapido, E., Suarez, L., . . . Mcalister, A. (2001). Cigarette smoking behavior among US Latino men and women from different countries of origin. American Journal of Public Health, 91(9), 1424-1430. doi: 10.2105/AJPH.91.9.1424 Perkins, R. B., Tipton, H., Shu, E., Marquez, C., Belizaire, M., Porter, C., . . . PierreJoseph, N. (2013). Attitudes toward HPV vaccination among low-income and minority parents of sons: A qualitative analysis. Clinical Pediatrics, 52(3), 231240. doi: 10.1177/0009922812473775 Pew Research Center. Second-Generation Americans. (2013). Retrieved from http://www.pewsocialtrends.org/2013/02/07/second-generation-americans/ Pham, K. L., Harrison, G. G., & Kagawa-Singer, M. (2007). Perceptions of diet and physical activity among California Hmong adults and youths. Preventing Chronic


211

Disease, 4(4), A93-A93. Retrieved from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2099291/ Polit, D. F., & Beck, C. T. (2010). Generalization in quantitative and qualitative research: Myths and strategies. International Journal of Nursing Studies, 47(11), 14511458. doi: 10.1016/j.ijnurstu.2010.06.004 Polly, R. K. (1992). Diabetes health beliefs, self-care behaviors, and glycemic control among older adults with non-insulin-dependent diabetes mellitus. The Diabetes Educator, 18(4), 321-327. doi: 10.1177/014572179201800411 Portes, A., & Rumbaut, R. (2006). Immigrant America: A Portrait. University of California Press. Retrieved from http://www.jstor.org/stable/10.1525/j.ctt7zw0nw Putsch, R. W., III, & Joyce, M. (1990). Dealing with patients from other cultures. In H. K. Walker & W. D. Hall (Eds.), Clinical Methods: The History, Physical and Laboratory Examinations (3rd ed., pp. 1-53). Boston, MA: Buttersworth. Retrieved from: https://www.ncbi.nlm.nih.gov/books/NBK340/?report=reader Ramo, D. E., & Prochaska, J. J. (2012). Broad reach and targeted recruitment using Facebook for an online survey of young adult substance use. Journal of Medical Internet Research, 14(1), e28. doi: 10.2196/jmir.1878 Reisi, M., Javadzade, S. H., Shahnazi, H., Sharifirad, G., Charkazi, A., & Moodi, M. (2014). Factors affecting cigarette smoking based on health-belief model structures in pre-university students in Isfahan, Iran. Journal of Education and Health Promotion, 3(23), 31-35. doi: 10.4103/2277-9531.127614 Remillard, M. L., Mazor, K. M., Cutrona, S. L., Gurwitz, J. H., & Tjia, J. (2014). Systematic review of the use of online questionnaires among the geriatric


212

population. Journal of the American Geriatrics Society, 62(4), 696–705. doi: 10.1111/jgs.12747 Rice, H. (2014). Fatalism. In Edward N. Zalta (ed.), The Stanford Encyclopedia of Philosophy (Summer 2015 Edition). Stanford, CA: Stanford University. Retrieved from: http://plato.stanford.edu/archives/sum2015/entries/fatalism/. Roche, K. M., Ghazarian, S. R., & Fernandez-Esquer, M. E. (2011). Unpacking acculturation: Cultural orientations and educational attainment among Mexicanorigin youth. Journal of Youth and Adolescence, 41(7), 920-931. doi: 10.1007/s10964-011-9725-8 Rodriguez-Reimann, D. I., Nicassio, P., Reimann, J. O., Gallegos, P. I., & Olmedo, E. L. (2004). Acculturation and health beliefs of Mexican Americans regarding tuberculosis prevention. Journal of Immigrant Health, 6(2), 51-62. doi: 10.1023/B:JOIH.0000019165.09266.71 Rogers, W. S. (1991). Explaining health and illness: An exploration of diversity. New York: Harvester Wheatsheaf. Rolland, J. S. (1987). Family illness paradigms: Evolution and significance. Family Systems Medicine, 5(4), 482-503. doi:10.1037/h0089735 Rolland, J. S. (1994). In sickness and in health: The impact of illness on couples' relationships. Journal of Marital and Family Therapy, 20(4), 327-347. doi:10.1111/j.1752-0606.1994.tb00125.x Romero, A. J., & Roberts, R. E. (1998). Perception of discrimination and ethnocultural variables in a diverse group of adolescents. Journal of Adolescence, 21(6), 641656. doi: 10.1006/jado.1998.0185


213

Roncancio, A. M., Ward, K. K., & Berenson, A. B. (2011). Hispanic women’s health care provider control expectations: The influence of fatalism and acculturation. Journal of Health Care for the Poor and Underserved, 22(2), 482–490. doi: 10.1353/hpu.2011.0038 Rosenstock, I. M. (1974). Historical origins of the health belief model. Health Education & Behavior, 2(4), 328-335. doi: 10.1177/109019817400200403 Rosenstock, I. M., Strecher, V. J., & Becker, M. H. (1988). Social learning theory and the health belief model. Health Education & Behavior, 15(2), 175-183. doi: 10.1177/109019818801500203 Rothstein, W. G., & Rajapaksa, S. (2003). Health beliefs of college students born in the United States, China, and India. Journal of American College Health, 51(5), 189194. doi: 10.1080/07448480309596350 Russell, N. K., & Roter, D. L. (1993). Health promotion counseling of chronic-disease patients during primary care visits. American Journal of Public Health, 83(7), 979–982. Retrieved from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1694773/pdf/amjph005310053.pdf Ryder, A. G., Alden, L. E., & Paulhus, D. L. (2000). Is acculturation unidimensional or bidimensional? A head-to-head comparison in the prediction of personality, selfidentity, and adjustment. Journal of Personality and Social Psychology, 79(1), 4965. doi: 10.1037/0022-3514.79.1.49.


214

Ryder, A. G., Alden, L. E., & Paulhus, D. L. (2000). Vancouver Index of Acculturation [Database record]. Retrieved from PsycTESTS. doi: http://dx.doi.org/10.1037/t03897-000 Sadler, G. R., Lee, H.-C., Seung-Hwan Lim, R., & Fullerton, J. (2010). Recruiting hardto-reach United States population sub-groups via adaptations of snowball sampling strategy. Nursing & Health Sciences, 12(3), 369–374. http://doi.org/10.1111/j.1442-2018.2010.00541.x Sam, D. L., & Berry, J. W. (2010). Acculturation when individuals and groups of different cultural backgrounds meet. Perspectives on Psychological Science, 5(4), 472-481. doi: 10.1177/1745691610373075 Santos, D. J. D. S., Palomares, N. B., Normando, D., & Quintão, C. C. A. (2010). Race versus ethnicity: Differing for better application. Dental Press Journal of Orthodontics, 15(3), 121-124. doi: 10.1590/S2176-94512010000300015 Sarmiento, I. A., & Cardemil, E. V. (2009). Family functioning and depression in lowincome Latino couples. Journal of Marital and Family Therapy, 35(4), 432-445. doi:10.1111/j.1752-0606.2009.00139.x Saunders, G. H., Frederick, M. T., Silverman, S., & Papesh, M. (2013). Application of the health belief model: Development of the hearing beliefs questionnaire (HBQ) and its associations with hearing health behaviors. International Journal of Audiology, 52(8), 558-567. doi: 10.3109/14992027.2013.791030 Scanlon, K., Harding, S., Hunt, K., Petticrew, M., Rosato, M., & Williams, R. (2006). Potential barriers to prevention of cancers and to early cancer detection among


215

Irish people living in Britain: A qualitative study. Ethnicity and Health, 11(3), 325-341. doi: 10.1080/13557850600628307 Scarinci, I. C., Bandura, L., Hidalgo, B., & Cherrington, A. (2012). Development of a theory-based (PEN-3 and health belief model), culturally relevant intervention on cervical cancer prevention among Latina immigrants using intervention mapping. Health Promotion Practice, 13(1), 29-40. Retrieved from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3982834/ Scott, J., & Marshall. (1998). Dominant culture. A Dictionary of Sociology. Retrieved November 2, 2016 from Encyclopedia.com: http://www.encyclopedia.com/socialsciences/dictionaries-thesauruses-pictures-and-press-releases/dominant-culture Schwartz, S. J., Unger, J. B., Zamboanga, B. L., & Szapocznik, J. (2010). Rethinking the concept of acculturation: Implications for theory and research. American Psychologist, 65(4), 237. doi: 10.1037/a0019330 Serovich, J. M. (2000). Helping HIV-positive persons to negotiate the disclosure process to partners, family members, and friends. Journal of Marital & Family Therapy, 26(3), 365-372. Shiloh, S., Rashuk-Rosenthal, D., & Benyamini, Y. (2002). Illness causal attributions: An exploratory study of their structure and associations with other illness cognitions and perceptions of control. Journal of Behavioral Medicine, 25(4), 373-394. doi: 10.1023/A:1015818532390 Shokar, N. K., Byrd, T., Lairson, D. R., Salaiz, R., Kim, J., Calderon-Mora, J., . . . Ortiz, M. (2015). Against colorectal cancer in our neighborhoods, a community-based colorectal cancer screening program targeting low-income Hispanics: Program


216

development and costs. Health Promotion Practice, 16(5), 656-666. doi: 10.1177/1524839915587265 Singh, G. K., Kogan, M. D., & Stella, M. Y. (2009). Disparities in obesity and overweight prevalence among US immigrant children and adolescents by generational status. Journal of Community Health, 34(4), 271-281. doi: 10.1007/s10900-009-9148-6 Sperry, L. (2007). Illness perceptions and receptivity to counseling: Implications for individual and couples therapy. The Family Journal, 15(3), 298-302. doi:10.1177/1066480707301619 Sperry, L. (1986). The ingredients of effective health counseling: Health beliefs, compliance and relapse prevention. Individual Psychology, 42(2), 279-287. Retrieved from: http://web.a.ebscohost.com.ezproxy.simpsonulibrary.org/ehost/pdfviewer/pdfview er?sid=c8911800-781a-4fa8-b43a494fb19d14e3%40sessionmgr4010&vid=0&hid=4207 Sobralske, M. C. (2006). Health care seeking among Mexican American men. Journal of Transcultural Nursing, 17(2), 129-138. doi: 10.1177/1043659606286767 Sood, E. D., Mendez, J. L., & Kendall, P. C. (2012). Acculturation, religiosity, and ethnicity predict mothers’ causal beliefs about separation anxiety disorder and preferences for help-seeking. Journal of Cross-Cultural Psychology, 43(3), 393409. doi:10.1177/0022022110389492 Soto, C., Unger, J. B., Ritt-Olson, A., Teran, L., Huang, T., Hoffman, B. R., & Palmer, P. (2011). Cultural values and substance use in a multiethnic sample of California


217

adolescents. Addiction Research & Theory, 10(3), 257-279. doi: 10.1080/16066350211869 Stanton, J. M. (1998). An empirical assessment of data collection using the Internet. Personnel Psychology, 51(3), 709–725. doi: 10.1111/j.1744-6570.1998.tb00259.x Suh, E. E. (2008). The sociocultural context of breast cancer screening among Korean immigrant women. Cancer Nursing, 31(4), E1. doi: 10.1097/01.NCC.0000305742.56829.fc Sundstrom, B., Carr, L. A., Demaria, A. L., Korte, J. E., Modesitt, S. C., & Pierce, J. Y. (2015). Protecting the next generation: Elaborating the Health Belief Model to increase HPV vaccination among college-age women. Social Marketing Quarterly, 21(3), 173-188. doi: 10.1177/1524500415598984 Swami, V., Arteche, A., Chamorro‐Premuzic, T., Maakip, I., Stanistreet, D., & Furnham, A. (2009). Lay perceptions of current and future health, the causes of illness, and the nature of recovery: Explaining health and illness in Malaysia. British Journal of Health Psychology, 14(3), 519-540. doi: 10.1348/135910708X370781 Swanson, K. M. (1993). Nursing as informed caring for the well‐being of others. Image: The Journal of Nursing Scholarship, 25(4), 352-357. doi: 10.1111/j.15475069.1993.tb00271.x Taylor, J. Y., Peternell, B., & Smith, J. A. (2013). Attitudes toward genetic testing for hypertension among African American women and girls. Nursing Research and Practice, 2013, 341374-341374. Retrieved from: https://pdfs.semanticscholar.org/834d/655074e8a4b30c17543aa001e78fc5119994 .pdf


218

Taylor, G., McNeill, A., Girling, A., Farley, A., Lindson-Hawley, N., & Aveyard, P. (2014). Change in mental health after smoking cessation: systematic review and meta-analysis. BMJ: British Medical Journal, 348, g1151. doi: 10.1136/bmj.g1151 Thompson, L. F., Surface, E. A., Martin, D. L., & Sanders, M. G. (2003). From paper to pixels: Moving personnel surveys to the Web. Personnel Psychology, 56(1), 197– 227. doi: 10.1111/j.1744-6570.2003.tb00149.x Tobiano, G., Bucknall, T., Marshall, A., Guinane, J., & Chaboyer, W. (2015). Patients' perceptions of participation in nursing care on medical wards. Scandinavian Journal of Caring Sciences, 30(2), 260. doi: 10.1111/scs.12237 Trankle, S. A., & Haw, J. (2010). Predicting Australian Health Behaviour from Health Beliefs. Electric Journal of Applied Psychology, 5(2). doi: 10.7790/ejap.v5i2.167 Trevelyan, E., Gambino, C., Gryn, T., Larsen, L., Acosta, Y., Grieco, E., . . . Walters, N. (2016, November). Characteristics of the U.S. Population by Generational Status: 2013. Retrieved May 12, 2017, from: https://www.census.gov/content/dam/Census/library/publications/2016/demo/P23 -214.pdf Trief, P. M., Himes, C. L., Orendorff, R., & Weinstock, R. S. (2001). The marital relationship and psychosocial adaptation and glycemic control of individuals with diabetes. Diabetes Care, 24(8), 1384-1389. doi: 10.2337/diacare.24.8.1384 Trompeta, J. A., Cooper, B. A., Ascher, N. L., Kools, S. M., Kennedy, C. M., & Chen, J. (2012). Asian American adolescents' willingness to donate organs and engage in


219

family discussion about organ donation and transplantation. Progress in Transplantation, 22(1), 33-41. doi:10.7182/pit2012328 Upthegrove, R., Atulomah, O., Brunet, K., & Chawla, R. (2012). Cultural and social influences of negative illness appraisals in first‐episode psychosis. Early Intervention in Psychiatry, 7(4), 399-406. doi: 10.1111/j.1751-7893.2012.00389.x Vallejos, Q., Strack, R. W., & Aronson, R. E. (2006). Identifying culturally appropriate strategies for educating a Mexican immigrant community about lead poisoning prevention. Family & Community Health, 29(2), 143-152. doi: 10.1097/00003727-200604000-00009 Van De Vijver, F. J. (2007). Cultural and gender differences in gender-role beliefs, sharing household task and child-care responsibilities, and well-being among immigrants and majority members in the Netherlands. Sex Roles, 57(11-12), 813824. doi: 10.1007/s11199-007-9316-z Velligan, D. I., Sajatovic, M., Hatch, A., Kramata, P., & Docherty, J. P. (2017). Why do psychiatric patients stop antipsychotic medication? A systematic review of reasons for nonadherence to medication in patients with serious mental illness. Patient Preference and Adherence, 11, 449–468. doi: 10.2147/PPA.S124658 Vrinten, C., Wardle, J., & Marlow, L. A. (2016). Cancer fear and fatalism among ethnic minority women in the United Kingdom. British Journal of Cancer, 114(5), 597– 604. doi: 10.1038/bjc.2016.15


220

Wade, D. T., & Halligan, P. W. (2004). Do biomedical models of illness make for good healthcare systems? BMJ : British Medical Journal, 329(7479), 1398–1401. doi: 10.1136/bmj.329.7479.1398 Wagner, J., Kuoch, T., Tan, H. K., Scully, M., & Rajan, T. V. (2013). Health beliefs about chronic disease and its treatment among aging Cambodian Americans. Journal of Cross-Cultural Gerontology, 28(4), 481-489. doi: 10.1007/s10823013-9214-5 Wagner, K. D., Unger, J. B., Bluthenthal, R. N., Andreeva, V. A., & Pentz, M. A. (2010). Cognitive behavioral theories used to explain injection risk behavior among injection drug users: A review and suggestions for the integration of cognitive and environmental models. Health Education & Behavior: The Official Publication of the Society for Public Health Education, 37(4), 504–532. doi: 10.1177/1090198109357319 Wagstaff, C. (2007). Towards understanding the self-perception of people with a psychotic illness who use illicit substances and have a history of disengagement from mental health services: Qualitative research. The International Journal of Psychiatric Nursing Research, 12(3), 1503-1520. Retrieved from: http://web.b.ebscohost.com.ezproxy.simpsonulibrary.org/ehost/pdfviewer/pdfvie wer?vid=8&sid=0a6e15bf-1d66-4e59-ba31-2c25e45c03cf%40sessionmgr101 Wang, J. H., Liang, W., Schwartz, M. D., Lee, M. M., Kreling, B., & Mandelblatt, J. S. (2007). Development and evaluation of a culturally tailored educational video: Changing breast cancer-related behaviors in Chinese women. Health Education & Behavior, 35(6), 806-820. doi: 10.1177/1090198106296768


221

Ward, C., & Geeraert, N. (2016). Advancing acculturation theory and research: The acculturation process in its ecological context. Current Opinion in Psychology, 8, 98-104. doi: 10.1016/j.copsyc.2015.09.021 Weinman, J., Petrie, K. J., Moss-Morris, R., & Horne, R. (1996). The illness perception questionnaire: a new method for assessing the cognitive representation of illness. Psychology and Health, 11(3), 431-445. doi: 10.1080/08870449608400270 White, S. H., Shonkoff, J. P., Braddock, J. H., Britt, D., Bumpass, L., Davis, P., ... & Michael, R. (1995). Immigrant children and their families-issues for research and policy. Future of Children, 5(2), 72-89. Retrieved from: https://www.princeton.edu/futureofchildren/publications/docs/05_02_06.pdf Wingren, C. J., Agardh, D., & Merlo, J. (2012). Acculturation and celiac disease risk in second-generation immigrants: a nationwide cohort study in Sweden. Scandinavian Journal of Gastroenterology, 47(10), 1174-1180. doi: 10.3109/00365521.2012.703238 Wright, K. B. (2005). Researching Internet‐based populations: Advantages and disadvantages of online survey research, online questionnaire authoring software packages, and web survey services. Journal of Computer‐Mediated Communication, 10(3), 00-00. doi: 10.1111/j.1083-6101.2005.tb00259.x Wu, T., & Ronis, D. (2009). Correlates of recent and regular mammography screening among Asian-American women. Journal of Advanced Nursing, 65(11), 24342446. doi: 10.1111/j.1365-2648.2009.05112.x


222

Yu, S. M., Huang, Z. J., & Singh, G. K. (2003). Health status and health services utilization among US Chinese, Asian Indian, Filipino, and other Asian/Pacific Islander Children. Pediatrics, 113(1), 101-107. doi: 10.1542/peds.113.1.101 Yarova, L. A., Krassen Covan, E., & Fugate-Whitlock, E. (2013). Effect of acculturation and health beliefs on utilization of health care services by elderly women who immigrated to the USA from the former Soviet Union. Health Care for Women International, 34(12), 1097-1115. doi:10.1080/07399332.2013.807259 Yu, M. Y., & Wu, T. Y. (2005). Factors influencing mammography screening of Chinese American women. Journal of Obstetric, Gynecologic, & Neonatal Nursing, 34(3), 386-394. doi: 10.1177/0884217505276256 Zagefka, H. (2009). The concept of ethnicity in social psychological research: Definitional issues. International Journal of Intercultural Relations, 33(3), 228241. Doi: 10.1016/j.ijintrel.2008.08.001 Zhao, M., Esposito, N., & Wang, K. (2010). Cultural beliefs and attitudes toward health and health care among Asian-born women in the United States. Journal of Obstetric, Gynecologic & Neonatal Nursing, 39(4), 370-385. doi: 10.1111/j.15526909.2010.01151.x

ACCULTURATION AND HEALTH BELIEFS Appendix

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Simpson University Institutional Review Board Informed Consent The Effects of Acculturation on SecondProject title: Generation Immigrants’ Health Beliefs Principal investigator:

Faculty advisor:

Robert Crowder, Master of Arts in Counseling Psychology Contact at: [email protected] Dr. Ashley Brimager, Assistant Professor – Department of Psychology Contact at: [email protected]

Researchers’ statement You are invited to participate in this research study. The purpose of this consent form is to provide you with the information that you need to make an informed decision regarding your potential participation. Please read the form carefully. If you have any questions about the purpose of the study, what we will be asking of you as part of this study, the potential risks and benefits to participation, your rights as a volunteer, or anything else that is not made clear regarding the study in this informed consent, you are welcome to contact the principal investigator at: [email protected]. Once your questions have been answered to your satisfaction, you may choose whether or not to participate in the study. This process is called “the informed consent.” If you would like a copy of this form for your records please contact the principal investigator at the email previously listed. Purpose Prior research has shown that aspects outside of one’s control, such as level of acculturation, can influence an individual’s beliefs. Because health beliefs are a type of belief, it is likely that health beliefs can be influenced in a similar way. Prior research has shown that there can be a difference, which has been attributed to level of acculturation, between the health beliefs of first-generation immigrants and third-generation immigrants. However, little research has been conducted investigating how acculturation may be correlated with the health beliefs of second-generation immigrants. This is important to investigate because an individual’s health beliefs can lead one to follow through on certain health behaviors; hence individual’s health beliefs are a point of research interest. This study will investigate the potential correlation between acculturation levels and health beliefs with a particular focus on the interaction for second-generation immigrants. Procedure

If you decide to participate in the study, you will complete a 40-question survey. It will assess your health beliefs as well as ask various cultural questions. It should take 5-10 minutes to complete the survey. You will first answer a few questions to gather pertinent demographic information. This information will include questions pertaining to the age group that you are a part of and what immigrant generation you best fit into. The second part of the survey you will answer numerous questions where you will decide how much you agree with a presented statement that is meant to measure various types of health beliefs. These statements include, but are not limited to, “I usually expect to take


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medicine to help me recover from an illness” and “How well or badly I look after myself generally has an influence on my overall health.” The final part of the survey you will choose how much you may agree with statements that are meant to assess your level of acculturation. These statements will include and be similar to “I often participate in my heritage cultural traditions” and “I enjoy typical American jokes and humor.” At the end of the survey you will have the opportunity to copy and paste a link that will take you to a separate survey where you can enter your email address to enter into the drawing for one of the five $10 gift cards. The step to require you entering your email into a separate survey is taken to ensure your anonymity in relation to your confidential answers on the survey. Risk, stress, or discomfort There is no known risk to participating in this study. It may be mildly stressful to complete this long of a survey. Also, some of the questions may provoke mild personal discomfort. Benefits There may be no potential personal benefit for you to participate in this study other than experiencing the research process. However, some individuals may find it beneficial to think about their health beliefs. However, the researchers believe that, in the future, the results of this study may be beneficial to society because it may help policy-makers and decision-makers better address health concerns and develop more appropriate health outreach programs. Compensation If you participate in this study, you will have the opportunity to potentially receive one of five $10 gift cards. In order to enter, you will need to click the link at the end of the survey to enter your email address. Recipients will be chosen at random and will be contacted via email. Odds of being a recipient will be determined by the amount of participants that enter. Anonymity The answers you provide in the study and any identifiable personal information, specifically your email address, will not be linked in anyway. The list of emails will be destroyed upon delivery of gifts cards to recipients to further ensure anonymity. Confidentiality Records of your participation in this study will be held confidential as far as is permitted by law. The records from this study will be available for review by members of the institutional review board at Simpson University (a committee that reviews and approves research studies). It is possible that these records could contain information that personally identifies you. In the event of any report or publication from this study, your


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identity will not be disclosed. Results will be reported in a summarized manner in such a way that you cannot be identified. Voluntary Participation Taking part in this research study is voluntary. You may refuse to participate and you are free to withdraw from this study at any time without penalty or loss of benefits that you may be entitled to. If you would prefer not to answer a question on the survey, you are free not to answer it.


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Subject’s statement Your signature indicates that you have read and understand the information provided above, that you willingly agree to participate, that you may withdraw your consent at any time and discontinue participation without penalty. You will receive a copy of this form. Participant’s name (printed): _________________________________________________ ___________________________________________ Signature of participant Date

_______________________

Researcher’s statement I have discussed the above points with the participant or, where appropriate, with the participant’s legally authorized representative, using a translator when necessary. It is my opinion that the participant understands the risks, benefits, and procedures involved with participation in this research study. ____________________________ Signature of Researcher

_____________________________ Date


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All information you provide on this document is tied only to a randomly selected participant number, it is confidential and cannot be used to identify you or against you in any way. The information you provide in this study will remain anonymous. 2) Date: 3) Your sex (circle one):

Male

Female

4) Please select what age group you fit into: 18-25, 26-30, 31-40, 41-50, 51-60, 61-70, 71 or older 5) Highest degree earned (AA, BA/BS, MA/MS, Ph.D., High School Diploma, Some schooling, Unknown):__________ 6) Generational Status: Of the following definitions for generational status please choose which applies most to you. First-generation immigrant (neither you or your parents were born in the USA) Second-generation immigrant (at least one of your parents was born in the USA and one was not) Third-generation immigrant or more (both of your parents were born in the USA) 7) Of the following races please select which you MOST identify? Hispanic/Latino

Black or African-American, non-Hispanic

White, non-

Hispanic American Indian or Alaskan Native, non-Hispanic

Asian, non-Hispanic

Native Hawaiian or other Pacific Islander, non-Hispanic

Two or more races, non-

Hispanic 8) What nationality do you most identify with? American

Mexican

Chinese

Dutch

Other:___________________ 9) Please list what county you currently live in: ______________


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Health Belief Questionnaire The following statements represent a variety of health beliefs. Please read each statement, and select how much you agree/disagree with the statement. 10. I believe I may have certain in built weaknesses which make me vulnerable to particular illness or disorders. Strongly agree Agree Somewhat Agree Neither Agree or Disagree Somewhat Disagree Disagree Strongly Disagree 11. When I’m ill enough to consult a doctor, my recovery will be faster if I comply properly with the advice and treatment I get. Strongly agree Agree Somewhat Agree Neither Agree or Disagree Somewhat Disagree Disagree Strongly Disagree 12. To improve my health would require improvements in the environment in which I live. Strongly agree Agree Somewhat Agree Neither Agree or Disagree Somewhat Disagree Disagree Strongly Disagree 13. When I’m ill, my recovery is influenced by the quality and comfort of my surrounding. Strongly agree Agree Somewhat Agree Neither Agree or Disagree Somewhat Disagree Disagree Strongly Disagree 14. I usually expect to take medicine to help me recover from illness. Strongly agree Agree Somewhat Agree Neither Agree or Disagree Somewhat Disagree


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Disagree Strongly Disagree 15. My state of health at any time is considerably influenced by whether or not I’ve been exposed to infectious or contagious disease organisms. Strongly agree Agree Somewhat Agree Neither Agree or Disagree Somewhat Disagree Disagree Strongly Disagree 16. God has given me the means by which to improve my health. Strongly agree Agree Somewhat Agree Neither Agree or Disagree Somewhat Disagree Disagree Strongly Disagree 17. How well or badly I look after myself generally has an influence on my overall health. Strongly agree Agree Somewhat Agree Neither Agree or Disagree Somewhat Disagree Disagree Strongly Disagree 18. The speed of my recovery from an infection depends a lot on the virulence of the disease organisms causing it. Strongly agree Agree Somewhat Agree Neither Agree or Disagree Somewhat Disagree Disagree Strongly Disagree 19. I see illness as a challenge to be overcome—a determined attitude on my part can speed my recovery. Strongly agree Agree Somewhat Agree Neither Agree or Disagree Somewhat Disagree Disagree Strongly Disagree


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Acculturation Please answer each question as carefully as possible by circling one of the numbers beneath each question to indicate your degree of agreement or disagreement. Many of these questions will refer to your heritage culture, meaning file culture that has influenced you most (other than American culture). It may be the culture of your birth, the culture in which you have been raised, or another culture that forms part of your background. If there are several such cultures, pick the one that has influenced you most (e.g., Irish, Chinese, Mexican, Black). If you do not feel that you have been influenced by any other culture, please try to identify a culture that may have had an impact on previous generations of your family. Use the following key to help guide your answers: Strongly Neutral/ disagree Disagree Depends Agree 1 2 3 4 5 6 7

8

Strongly Agree 9

20. Please write your heritage culture in the space provided 21. I often participate in my heritage cultural traditions. 1 2 3 4 5 6 7 22. I often participate in mainstream American cultural traditions 1 2 3 4 5 6 7

ACCULTURATION AND HEALTH BELIEFS 23. I would be willing to marry a person from my heritage culture 1 2 3 4 5 6 7 24. I would be willing to marry an American person 1 2 3 4 5 6 7 25. I enjoy social activities with people from the same heritage culture as myself 1 2 3 4 5 6 7 26. I enjoy social activities with typical American people 1 2 3 4 5 6 7

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ACCULTURATION AND HEALTH BELIEFS 27. I am comfortable working with people of the same heritage culture as myself 1 2 3 4 5 6 7 28. I am comfortable working with typical American people 1 2 3 4 5 6 7 29. I enjoy entertainment (e.g., movies, music) from my heritage culture 1 2 3 4 5 6 7 30. I enjoy American entertainment (e.g., movies, music) 1 2 3 4 5 6 7

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ACCULTURATION AND HEALTH BELIEFS 31. I often behave in ways that are typical of my heritage culture 1 2 3 4 5 6 7 32. I often behave in ways that are 'typically American' 1 2 3 4 5 6 7 33. It is important for me to maintain or develop the practices of my heritage culture 1 2 3 4 5 6 7 34. It is important for me to maintain or develop American cultural practices 1 2 3 4 5 6 7

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ACCULTURATION AND HEALTH BELIEFS 35. I believe in the values of my heritage culture 1 2 3 4 5 6 7 36. I believe in mainstream American values 1 2 3 4 5 6 7 37. I enjoy the jokes and humor of my heritage culture 1 2 3 4 5 6 7 38. I enjoy typical American jokes and humor 1 2 3 4 5 6 7

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39. I am interested in having friends from my heritage culture 1 2 3 4 5 6 7 40. I am interested in having American friends 1 2 3 4 5 6 7 End of Survey You are at the end of the survey. Please make sure to "finish the survey" so that your responses can be collected, but prior to doing so either type the following web link: https://www.surveymonkey.com/r/983CC7C into a separate webpage or copy and paste it into a separate webpage.