Assessing Community Resilience: An Application of ...

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research-article2014

ABSXXX10.1177/0002764214550295American Behavioral ScientistPfefferbaum et al.

Community Resilience Assessment and Intervention

Assessing Community Resilience: An Application of the Expanded CART Survey Instrument With Affiliated Volunteer Responders

American Behavioral Scientist 2015, Vol. 59(2) 181–199 © 2014 SAGE Publications Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0002764214550295 abs.sagepub.com

Rose L. Pfefferbaum1, Betty Pfefferbaum2, Pascal Nitiéma2, J. Brian Houston3, and Richard L. Van Horn2

Abstract This article describes an application of the Communities Advancing Resilience Toolkit Assessment Survey using a sample of affiliated volunteer responders. The Communities Advancing Resilience Toolkit Assessment Survey is a theory-based, evidence-informed instrument. Early applications of the survey identified four domains: Connection and Caring, Resources, Transformative Potential, and Disaster Management. The version of the instrument used in the current application added items related to Information and Communication, thus creating a fifth domain. The application confirmed the fivefactor model and the instrument demonstrated good reliability. Affiliated volunteer responders served as key informants regarding community resilience because of their involvement in local disaster readiness and response. Home ownership and active membership in an affiliated volunteer responder group were associated with the total community resilience score and with multiple domain scores, suggesting the importance of community member investment and engagement for a community’s resilience. Although the study sample involved affiliated volunteer responders, it is likely that engagement in other community organizations and activities may yield similar benefits for resilience. 1Phoenix

Community College, Phoenix, AZ, USA of Oklahoma Health Sciences Center, Oklahoma City, OK, USA 3University of Missouri, Columbia, MO, USA 2University

Corresponding Author: Betty Pfefferbaum, Department of Psychiatry and Behavioral Sciences, College of Medicine, University of Oklahoma Health Sciences Center, P.O. Box 26901, WP3217, Oklahoma City, OK 73126-0901, USA. Email: [email protected]

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Keywords Communities Advancing Resilience Toolkit (CART), community assessment, community resilience, disaster management, volunteer responders

Introduction Relatively few interventions are available to measure and build community resilience (Chandra et al., 2010) despite increasing attention in recent years to the importance of community resilience in disaster management policy, planning, and practice. One intervention, the Communities Advancing Resilience Toolkit (CART; B. Pfefferbaum, Van Horn, & Pfefferbaum, 2014; R. L. Pfefferbaum, Pfefferbaum, & Van Horn, 2011, 2013) has been recognized as an “important” community tool to assist communities in enhancing resilience (Chandra et al., 2011, p. 2). CART is a publicly-available, community-driven intervention that consists of a strategic planning process for building community resilience to disasters with instruments for collecting and using assessment data to develop and implement resilience-building strategies. The CART Assessment Survey, one of nine CART instruments, is a field-tested questionnaire for assessing a community’s resilience. The survey instrument can be used to obtain baseline information about a community, identify relative community resilience strengths and challenges, and reexamine a community after a disaster or intervention. As described in the current online CART manual (R. L. Pfefferbaum, Pfefferbaum, & Van Horn, 2013), the CART Assessment Survey is based on a four-factor model of community resilience characterized by four interrelated CART domains: (a) Connection and Caring (including relatedness, participation, shared values, support and nurturance, equity, justice, hope, and diversity); (b) Resources (including natural, physical, information, human, social, and financial resources); (c) Transformative Potential (deriving from the ability of communities to frame collective experiences, collect and analyze relevant data, assess community performance, and build skills); and (d) Disaster Management (addressing prevention and mitigation, preparedness, response, and recovery). Recognizing the importance of information and communication in community resilience, one goal of the current study was to confirm the existence of a fifth domain, Information and Communication, addressing the availability of information and trust in public officials. Clearly, during emergencies people need accurate information about the dangers they face and the behavioral options available to them, information that should be communicated by a trusted source (Norris, Stevens, Pfefferbaum, Wyche, & Pfefferbaum, 2008). Evidence from early CART field test samples (R. L. Pfefferbaum, Neas, Pfefferbaum, Norris, & Van Horn, 2013) suggested that information and communication were important elements of each of the original four domains rather than a separate domain. For example, communication can foster connection and caring; information and communication channels contribute to a community’s resource base; communication is necessary to transmit information that contributes to critical reflection, skill building, and transformation; and information and effective communication are essential for disaster management. A recent study using the CART Assessment

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Survey among school principals on the coast of the United States (Sherrieb et al., 2012) included specific survey items addressing information and communication. That study found Information and Communication to be one of four adaptive capacities including Social Capital, Economic Development, and Community Competence as well. Thus, in an effort to confirm Information and Communication as a separate CART domain, the present study relied on a five-factor model of community resilience based on the five domains and included specific survey items to address information and communication in an expanded CART Assessment Survey. This article describes an application of the expanded CART Assessment Survey with affiliated volunteer responders in a small, densely populated state in the northeast region of the U.S. Affiliated volunteer responders served as key informants regarding community resilience because of their involvement in local disaster readiness and response. They constituted a valuable sample with special knowledge, experience, and perspective that could help identify and clarify variables associated with community resilience. The emphasis in this article is on the survey instrument itself and its use with affiliated volunteer responders rather than on the resilience of specific communities involved.

Method Instrument The CART Assessment Survey instrument is a theory-based (R. L. Pfefferbaum, Pfefferbaum, Van Horn, Klomp, et al., 2013), evidence-informed questionnaire (R. L. Pfefferbaum, Neas, et al., 2013) that consists of core community resilience items, questions related to study participant demographics, and other questions addressing, for example, emergency experience, sources of emergency assistance, and reasons for feeling connected to the community. The design of the instrument facilitates the addition of items to address concerns of local sponsoring organizations, in this case representatives of the state’s emergency management agency and leaders of the participating affiliated volunteer responder programs. The version of the instrument used in this study included 24 core community resilience items addressing 5 interrelated community resilience domains. Four of the 24 core community resilience items dealt with information and communication, constituting the Information and Communication domain.

Survey Administration and Sample The survey was administered in late 2013 to members of affiliated volunteer responder groups (i.e., volunteer responders who work together in organized groups, such as Community Emergency Response Teams and Medical Reserve Corps, rather than individuals who respond spontaneously to disasters). For the most part, affiliated volunteer responder groups surveyed in this study were community based. An email endorsing the survey and including a link to the online survey was sent by the state emergency management agency to affiliated volunteer group leaders inviting

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participation of local group members. The instrument and process were approved by the Institutional Review Board of the University of Oklahoma Health Sciences Center.

Data Analysis The sample analyzed in this report included 64 participants. The percentage of missing data per variable in the data set ranged from 0.0% (n = 0) to 12.5% (n = 8). The missing data pattern was completely at random (Little’s missing completely at random test, chi-square = 255.50; df = 257; 1 − p = .5147), therefore ignorable (Little, 1988). No imputation of the missing values was performed. Response options for the 24 core community resilience items ranged from 1 (strongly disagree) to 5 (strongly agree), with a midpoint of 3 (neither disagree nor agree). Percentage of agreement with a survey item required a response of agree or strongly agree; responses of strongly disagree, disagree, and neither disagree nor agree were considered not in agreement with the item statement. High and low percentage of agreement scores for the 24 community resilience items were used to identify the primary community resilience strength and the primary community resilience challenge, respectively. A study participant’s domain score was calculated by summing the scores of the items composing that domain. The total community resilience score was computed by summing the scores of the five domains. Factors associated with each of the five domains, the total community resilience score, the primary community resilience strength, and the primary community resilience challenge were identified. Variables examined included demographic characteristics (sex, age, marital status, employment status, and home ownership) and disaster-related characteristics (disaster experience, completion of basic training as part of membership in an affiliated volunteer responder group, disaster response experience, active membership in an affiliated volunteer responder group, and interest in being deployed within the community). Comparisons of means of continuous variables were made with the Wilcoxon ranksum test (t approximation). Fisher’s exact test was used to compare proportions. Pearson product–moment was used to compute correlations between continuous variables, and Fisher’s z transformation was used to estimate 95% confidence intervals (CIs) and p values for these correlations. The statistical power of the Wilcoxon ranksum test for detecting a large effect size was calculated using the Lehmann (1975) method, with the effect size index d set to 0.8. The reliability of the expanded survey instrument was determined using Cronbach’s alpha, with its 95% CI computed using the method proposed by Feldt, Woodruff, and Salih (1987). The level of statistical significance was set at α = .05. All p values reported in this analysis are two-sided. A Bayesian confirmatory factor analysis (BCFA) was used to determine whether the five-factor model of community resilience was confirmed by the collected data. Bayesian methodology was selected because the data were on an ordinal scale, and BCFA has been shown to handle adequately categorical variables (Asparouhov & Muthén, 2010). Vague priors were provided for the estimation of the parameters of interest. The posterior distributions of the model parameters were generated through a

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Markov Chain Monte Carlo procedure using the Metropolis–Hastings algorithm. The step size ε was set to 0.246 to target the optimal acceptance rate of 0.234 proposed by Roberts, Gelman, and Gilks (1997). A total of 300,000 iterations were performed (no burn-in) and thinned five times at a rate of two due to computer memory limitations. A final simulated sample of 50,000 observations was used for the statistical inferences. The goodness of fit of the BCFA model was assessed with the posterior predictive probability (PPP; Gelman, Meng, & Stern, 1996). A value of PPP close to 0.50 indicates a good fit of the model for the collected data. Statistical inferences about the model parameters were made with the simulated posterior distributions, which were summarized with the distribution mean and its 95% Bayesian credible interval. SPSS Analysis of Moment Structures (AMOS; Version 21.0, IBM Corporation, Armonk, NY) was used to conduct the BCFA. G*Power Version 3.1.4 (Faul, Erdfelder, Lang, & Buchner, 2007) was used to calculate the achieved statistical power of the Wilcoxon rank-sum tests, and SAS (Version 9.3, SAS Institute, Cary, NC) was used for data management, summary statistics, and bivariate analyses.

Results Characteristics of Study Participants Approximately half of the 64 participants were male (n = 33, 51.6%), and many were more than 50 years of age (n = 28, 43.8%). The great majority reported being White/ Caucasian (not of Hispanic origin), with few reporting being of another ethnicity (n = 3, 4.7%). Most participants were employed, with over half employed full time (n = 35, 54.7%) and fewer employed part time (n = 10, 15.6%). The majority reported being married. See Table 1. Study participants were asked about their disaster experience, disaster response experience, disaster management training, active membership in an affiliated volunteer response group, and interest in being deployed within the community. Most of the participants reported having personally experienced a natural disaster (n = 41, 64.1%), and one fourth reported they had no personal disaster experience (n = 16, 25.0%). Almost half of study participants indicated their most recent disaster response experience was as a professional responder (n = 30, 46.9%); almost one fifth responded most recently as a volunteer (n = 12, 18.8%); and only one individual reported no response experience (1.6%). Most participants were currently active members of an affiliated volunteer responder group (n = 45, 70.3%), most had completed basic disaster management training as part of their membership in a volunteer responder group (n = 55, 85.9%), and most were interested in being deployed within their community (n = 55, 85.9%). See Table 2. Study participants were asked what connects them to their community. More than half indicated they owned their home (n = 37, 57.8%) and had friends in the community (n = 35, 54.7%). Home ownership (n = 17, 26.6%) and family (n = 17, 26.6%) were the primary sources of connection. See Table 3 for additional information about sources of connection to community.

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Table 1. Demographics of the Study Participants. Frequency Sex Female Male Missing Age groups 21-30 years 31-40 years 41-50 years 51-60 years 61-70 years 71+ years Missing Ethnicity White Non-White Missing Employment status Full-time employed Part-time employed Retired Unemployed Missing Marital status Married Single/never married Divorced/separated/widowed Missing

Percentage

25 33 6

39.1 51.6 9.4

5 8 15 12 10 6 8

7.8 12.5 23.4 18.8 15.6 9.4 12.5

56 3 5

87.5 4.7 7.8

35 10 8 6 5

54.7 15.6 12.5 9.4 7.8

35 15 9 5

54.7 23.4 14.1 7.8

Perceptions of Community Resilience Survey items associated with each of the five CART domains, along with percentage of agreement for each item, are presented in Table 4. The highest percentage of agreement (81.3% agreement), and thus the primary community resilience strength, was associated with survey item B19: “My community can provide emergency services during a disaster.” The lowest percentage of agreement (32.8% agreement), and thus the primary community resilience challenge, was associated with survey item B24: “People in my community trust public officials.” See Table 4. Table 5 presents bivariate analyses involving demographic characteristics of the study participants, and Table 6 presents bivariate analyses involving participants’ disaster-related characteristics. Associations calculated for each of the five domains indicate that the Connection and Caring score was higher for home owners (Wilcoxon

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Table 2. Disaster Experience, Training, Active Membership, and Interest in Deployment. Frequency

Percentage

disastera

Has personally experienced a Natural disaster 41 64.1 Technological disaster 1 1.6 Human-caused disaster 3 4.7 No 16 25.0 Missing 3 4.7 Has responded to a disasterb As a volunteer responder 12 18.8 As a professional responder 30 46.9 Other 18 23.1 No 1 1.6 Missing 13 4.7 Has completed basic training associated with membership in an affiliated volunteer responder group Yes 55 85.9 No 6 9.4 Missing 3 4.7 Currently an active member of an affiliated volunteer responder group Yes 45 70.3 No 14 21.9 Missing 5 7.8 Interested in deployment within community Yes 55 85.9 No 5 7.8 Missing 4 6.3 aParticipants were asked to report the disaster that had the greatest impact on them if they had experienced more than one disaster. bParticipants were asked to report the most recent disaster if they had responded to more than one disaster.

p = .0058) and active members of affiliated volunteer responder groups (Wilcoxon p = .0264); the Resources score was higher for home owners (Wilcoxon p = .0081); the Transformative Potential score was higher for home owners (Wilcoxon p = .0303) and those who had not completed basic training as part of membership in an affiliated volunteer responder group (Wilcoxon p = .0418); the Disaster Management score was higher for females (Wilcoxon p = .0369) and those who were not married (Wilcoxon p = .0339); and the Information and Communication score was higher for active members of affiliated volunteer responder groups (Wilcoxon p = .0180). The total community resilience score was positively associated with home ownership (Wilcoxon p = .0051) and lack of interest in being deployed within the community (Wilcoxon p = .0374). There were no significant associations between participant demographics or disaster-related characteristics and the primary strength score. The primary challenge

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Table 3. Sources of Connection to Community. Sources of connection Own my home Friend Family Convenient location Work Faith-based organization Cannot afford to move School Civic club Military base Other Missing

Primary source of connection

Frequency

Percentagea

Frequency

Percentage

37 35 30 26 23 11

57.8 54.7 46.9 40.6 35.9 17.2

17 7 17 3 3 1

26.6 10.9 26.6 4.7 4.7 1.6

6 6 4 1 11b

9.4 9.4 6.3 1.6 17.2

3 2 1 0 5c 5

4.7 3.1 1.6 0.0 7.8 7.8

aThe

total percentage is more than 100% since more than one response option was allowed. bOther responses were: volunteer activities; I like the area; an enriched lifestyle; love it here; love my town; fire department and ambulance volunteer; great place to live; upside down on my mortgage; nothing; I am looking for a place to move; married; I grew up here. cOther responses were: volunteer activities; sense of community; no connection; sports; I grew up here.

score was negatively associated with interest in being deployed within the community (Wilcoxon p = .0276). Indeed, participants who were interested in being deployed in their community were less likely to agree that people in their community trust public officials compared with participants not interested in being deployed (31.5% vs. 80.0%; Fisher’s exact test, p = .0495). See Tables 5 and 6.

Reliability of the CART Assessment Survey Community resilience was measured with 24 core community resilience items incorporated in five subscales (domains). The reliability statistics (Cronbach’s alpha) were 0.83 (95% CI = 0.75, 0.89) for the Connection and Caring subscale; 0.75 (95% CI = 0.64, 0.84) for the Resources subscale; 0.90 (95% CI = 0.85, 0.93) for the Transformative Potential subscale; 0.91 (95% CI = 0.87, 0.94) for the Disaster Management subscale; and 0.83 (95% CI = 0.75, 0.89) for the Information and Communication subscale.

Community Resilience Domains The five-factor model of community resilience, incorporating the Information and Communication domain, was confirmed through a BCFA in this study sample, with a goodness of fit statistic (PPP) of 0.50 which is satisfactory. The summary statistics for the five domains scores are presented in Table 7.

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Pfefferbaum et al. Table 4. Percentage of Agreement With Community Resilience Items. Domain Connection and Caring

Resources

Transformative Potential

Disaster Management

Information and Communication

Item B1. People in my community feel like they belong to the community. B2. People in my community are committed to the well-being of the community. B3. People in my community have hope about the future. B4. People in my community help each other. B5. My community treats people fairly no matter what their background is. B6. My community supports programs for children and families. B7. My community has resources it needs to take care of community problems (resources include, for example, money, information, technology, tools, raw materials, and services). B8. My community has effective leaders. B9. People in my community are able to get the services they need. B10. People in my community know where to go to get things done. B11. My community works with organizations and agencies outside the community to get things done. B12. People in my community communicate with leaders who can help improve the community. B13. People in my community work together to improve the community. B14. My community looks at its successes and failures so it can learn from the past. B15. My community develops skills and finds resources to solve its problems and reach its goals. B16. My community has priorities and sets goals for the future. B17. My community tries to prevent disasters. B18. My community actively prepares for future disasters. B19. My community can provide emergency services during a disaster. B20. My community has services and programs to help people after a disaster. B21. My community keeps people informed (for example, via television, radio, newspaper, Internet, phone, neighbors) about issues that are relevant to them. B22. If a disaster occurs, my community provides information about what to do. B23. I get information/communication through my community to help with my home and work life. B24. People in my community trust public officials.

Percent agreement 79.7 64.1 64.1 78.1 53.1 71.9 53.1

50.0 48.4 46.9 54.7 56.3 67.2 48.4 56.3 56.3 64.1 59.4 81.3 54.7 73.4

60.9 45.3 32.8

There were significant positive correlations between all pairings of the five community resilience domains, with the highest between Resources and Transformative Potential and the lowest between Connection and Caring and Disaster Management. See Table 8.

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Table 5. Bivariate Analyses Between Community Resilience Scores and Demographic Characteristics of Study Participantsa. Community resilience dimension

Married (n = 35) Employed/retired Owns home ≤50 years All Males (n = 33) (n = 28) vs. >50 vs. not married vs. females (n = 17) vs. participants (n = 53) vs. (n = 24)b years (n = 28) (n = 64) (n = 25) unemployed (n = 6) not (n = 47)

Connection and Caring

19.1 (3.5) vs. 18.3 (2.9), p = .4298 Resources 17.6 (2.7) vs. 17.0 (3.1), p = .5659 Transformative 20.6 (4.4) vs. Potential 20.8 (4.3), p = .8037 Disaster 14.2 (3.1) vs. Management 15.9 (3.0), p = .0369* Information and 13.7 (3.0) vs. Communication 13.9 (3.1), p = .9419 Total score 84.6 (13.6) vs. 85.8 (14.0), p = .7101 0.83 Statistical power to detect a large effect size (d = 0.8)

18.5 (2.4) vs. 19.3 (3.7), p = .5765 17.5 (2.3) vs. 17.5 (3.5), p = .8565 21.2 (3.7) vs. 20.8 (4.6), p = .9309 14.8 (2.5) vs. 15.0 (3.6), p = .5086 13.8 (3.1) vs. 14.0 (3.0), p = 1.00 84.8 (11.4) vs. 86.8 (15.5), p = .2942 0.82

18.9 (3.8) vs. 18.6 (2.3), p = .7555 17.3 (2.8) vs. 17.6 (3.0), p = .4725 19.9 (3.9) vs. 21.9 (4.7), p = .0802 14.2 (3.1) vs. 16.0 (3.0), p = .0339* 13.4 (3.0) vs. 14.5 (2.8), p = .2036 83.7 (14.0) vs. 87.7 (13.0), p = .3098 0.83

20.6 (2.2) vs. 18.8 (3.1) 18.1 (3.2), p = .0058* 18.8 (1.8) vs. 17.5 (2.8) 17.0 (3.0), p = .0081* 22.6 (1.9) vs. 20.8 (4.2) 20.1 (4.7), p = .0303* 15.9 (2.4) vs. 15.0 (3.1) 14.7 (3.3), p = .1232 13.7 (3.0) vs. 15.0 14.5 (1.8) vs. 13.8 (3.0) (2.0), p = .3179 13.5 (3.3), p = .2206 84.9 (13.6) vs. 88.8 92.5 (7.6) vs. 85.5 (13.3) (14.2), p = .3374 82.6 (14.1), p = .0051* 0.43 0.78 — 18.6 (3.2) vs. 20.0 (3.3), p = .1260 17.5 (2.8) vs. 17.0 (3.2), p = 1.00 20.7 (4.4) vs. 21.0 (3.9), p = .8655 14.8 (3.1) vs. 15.8 (3.3), p = .3973

aWilcoxon

rank sum test (t approximation) was used. b“Not married” includes participants who described themselves as never married, single, divorced, separated, or widowed. *Statistically significant at .05.

Discussion A major goal of the present study was to consider a five-factor model of community resilience, modifying the four-factor CART model by adding information and communication survey items to the CART Assessment Survey. The study relied on affiliated volunteer responders as key informants. Although the intent was not to study the resilience of specific communities per se, the associations found between various community resilience scores (e.g., domain scores) and study participant characteristics (including demographics and disaster-related issues) also were examined. The fivefactor model, use of affiliated volunteer responders as key informants, and associations between community resilience scores and participant characteristics are discussed.

Confirmation of the Five-Factor Model This application of the expanded CART Assessment Survey confirmed the CART model of community resilience characterized by five interrelated domains: Connection and Caring, Resources, Transformative Potential, Disaster Management, and Information and Communication. There were significant positive correlations

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Table 6. Bivariate Analyses Between Community Resilience Scores and Disaster-Related Characteristics of Study Participantsa. Community resilience dimension Connection and Caring Resources

Transformative Potential Disaster Management Information and Communication Total score

Statistical power to detect a large effect size (d = 0.8)

Has experienced a disaster (n = 45) vs. no (n = 16)

Has completed basic training (n = 55) vs. no (n = 6)

18.6 (2.6) vs. 19.1 (4.5), p = .2818 17.3 (2.6) vs. 17.8 (3.5), p = .5875 21.1 (4.0) vs. 20.1 (5.1), p = .8510 14.8 (2.9) vs. 15.6 (3.5), p = .1213 13.6 (2.9) vs. 14.4 (2.8), p = .2275 84.9 (12.3) vs. 87.1 (16.2), p = .2553 0.75

18.6 (3.3) vs. 19.8 (2.7), p = .3716 17.4 (2.9) vs. 17.8 (1.8), p = .8269 20.5 (4.3) vs. 23.8 (2.2), p = .0418* 14.9 (3.2) vs. 15.7 (1.9), p = .7099 13.7 (3.0) vs. 15.2 (1.6), p = .2765 84.7 (13.8) vs. 92.3 (6.1), p = .2250 0.43

Has responded to a disaster (n = 49) vs. no (n = 12) 18.9 (3.0) vs. 18.0 (4.0), p = .5137 17.6 (2.7) vs. 16.8 (3.3), p = .6623 21.0 (4.0) vs. 20.2 (5.4), p = .9699 14.8 (3.0) vs. 15.8 (3.5), p = .1635 13.6 (3.1) vs. 14.7 (1.9), p = .2429 85.5 (12.9) vs. 85.4 (15.9), p = .5162 0.66

Active member (n = 45) vs. not active member (n = 14)

Interested in deployment in community (n = 55) vs. not interested (n = 5)

19.3 (3.2) vs. 16.9 (3.0), p = .0264* 17.8 (2.7) vs. 16.4 (3.1), p = .0762 21.2 (3.9) vs. 19.2 (5.1), p = .1072 15.4 (3.0) vs. 13.8 (3.4), p = .0646 14.3 (2.8) vs. 12.4 (3.0), p = .0180* 87.5 (12.4) vs. 78.7 (15.0), p = .0509 0.71

18.6 (3.3) vs. 20.0 (2.8), p = .3040 17.3 (2.9) vs. 18.6 (1.7), p = .2912 20.5 (4.4) vs. 23.2 (1.8), p = .1026 14.8 (3.1) vs. 17.2 (2.2), p = .1011 13.5 (2.8) vs. 17.2 (1.8), p = .1624 84.3 (13.6) vs. 96.2 (6.6), p = .0374* 0.43

aWilcoxon rank sum test (t approximation) was used. *Statistically significant at .05

between all pairings of the five community resilience domains, and the expanded survey instrument demonstrated good reliability in the current study. The five-factor CART model of community resilience and the current instrument are consistent with the attributes underlying early versions of the CART Assessment Survey, including (a) Connectedness, Commitment, and Shared Values; (b) Participation; (c) Support and Nurturance; (d) Structure, Roles, and Responsibilities; (e) Resources; (f) Critical Reflection and Skill Building; and (g) Communication. The theoretical foundation of the CART Assessment Survey (R. L. Pfefferbaum, Pfefferbaum, Van Horn, Klomp, et al., 2013) and these attributes have their basis in the literature on community capacity and competence (see, e.g., Cottrell, 1976; Gibbon, Labonte, & Laverack, 2002; Goeppinger & Baglioni, 1985; Goodman et al., 1998; Labonte & Laverack, 2001a, 2001b). In the development of CART, information and communication were considered important to community resilience, and initial versions of the survey instrument included items addressing them. Factor analysis of early CART samples grouped specific questions about information and communication with the original four CART domains (R. L. Pfefferbaum, Neas, et al., 2013). Ultimately, most of the information and communication items were eliminated to shorten the instrument. On

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Table 7. Five-Factor Model of Community Resilience. Domain Connection and Caring

Resources


Disaster Management


Item B1. People in my community feel like they belong to the community. B2. People in my community are committed to the well-being of the community. B3. People in my community have hope about the future. B4. People in my community help each other. B5. My community treats people fairly no matter what their background is. B6. My community supports programs for children and families. B7. My community has resources it needs to take care of community problems (resources include, for example, money, information, technology, tools, raw materials, and services). B8. My community has effective leaders. B9. People in my community are able to get the services they need. B10. People in my community know where to go to get things done. B11. My community works with organizations and agencies outside the community to get things done. B12. People in my community communicate with leaders who can help improve the community. B13. People in my community work together to improve the community. B14. My community looks at its successes and failures so it can learn from the past. B15. My community develops skills and finds resources to solve its problems and reach its goals. B16. My community has priorities and sets goals for the future. B17. My community tries to prevent disasters. B18. My community actively prepares for future disasters. B19. My community can provide emergency services during a disaster. B20. My community has services and programs to help people after a disaster. B21. My community keeps people informed (for example, via television, radio, newspaper, Internet, phone, neighbors) about issues that are relevant to them. B22. If a disaster occurs, my community provides information about what to do. B23. I get information/communication through my community to help with my home and work life. B24. People in my community trust public officials.

Loading (95% Bayesian credible interval) 0.64 (0.44, 0.79) 0.83 (0.70, 0.92) 0.60 (0.37, 0.77) 0.85 (0.73, 0.93) 0.67 (0.48, 0.81) 0.50 (0.26, 0.71) 0.37 (0.08, 0.61)

0.79 (0.64, 0.89) 0.70 (0.50, 0.85) 0.66 (0.44, 0.82) 0.71 (0.54, 0.83) 0.70 (0.54, 0.83) 0.79 (0.65, 0.88) 0.82 (0.70, 0.90) 0.80 (0.67, 0.89) 0.82 (0.70, 0.90) 0.82 (0.70, 0.90) 0.96 (0.91, 0.99) 0.86 (0.77, 0.92) 0.81 (0.70, 0.89) 0.86 (0.75, 0.93)

0.89 (0.79, 0.96) 0.85 (0.74, 0.93) 0.47 (0.22, 0.67)

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Table 8. Observed Correlations Between the Community Resilience Domains (95% CI). Domain Connection and Caring Resources Transformative Potential Disaster Management Information and Communication

Connection and Caring

Resources


Disaster Management

1.00 0.59 (0.40, 0.73), p < .0001 0.61 (0.42, 0.75), p < .0001 0.31 (0.06, 0.52), p = .0165 0.50 (0.28, 0.67), p < .0001


1.00 0.74 (0.60, 0.84), p < .0001 0.61 (0.43, 0.75), p < .0001 0.60 (0.41, 0.74), p < .0001

1.00 0.64 (0.46, 0.77), p < .0001 0.70 (0.55, 0.81), p < .0001

1.00

0.71 (0.55, 0.81), p < .0001

1.00

further consideration and as a result of the finding that Information and Communication was one of four adaptive capacities in the study of school principals on the U.S. coast (Sherrieb et al., 2012), information and communication items were included in the instrument used in the current study. Survey items specifically addressing information and communication and one dealing with trust in public officials were included in the Information and Communication domain, similar to the study by Sherrieb et al. (2012). Information and communication are vital to disaster management and to community resilience. If information is to be valued by members of the community leading, for example, to adherence with public recommendations and directives, the information should be communicated by a trusted source. Community resilience is reinforced by clear, accurate, timely, and effective communication among community members, between authorities and community members, and across community boundaries (B. J. Pfefferbaum, Reissman, Pfefferbaum, Klomp, & Gurwitch, 2007) during normal times as well as in relation to disasters. Information and communication, which are essential for community assessment and skill building, also can facilitate the identification and resolution of needs and the expression of opinions among community members, encourage local stakeholders to become involved in community problem solving, improve critical thinking, and foster trust in leadership. Given the importance of information and communication in community resilience, the addition of the Information and Communication domain seems appropriate, especially in light of the confirmation of the five-factor model in this study. The finding of significant positive correlations between all pairings of the five community resilience domains was expected because the domains are interrelated. That the highest correlation was between Resources and Transformative Potential suggests the importance of resources in enabling a community to undertake self-study, develop skills, and find resources to reach its goals. The relatively low correlation between Connection and Caring and Disaster Management may reflect the fact that disaster management exists more in the realm of an organized system (or systems) than in people’s sense of belonging, commitment, hope, and willingness to help each other. It is unclear how these findings might differ using community samples other than one

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composed of affiliated volunteer responders. Future research should implement the CART Assessment Survey with other community samples to examine similarities across, and differences between, samples.

Affiliated Volunteer Responders as Key Informants The use of key informants, as individuals who are knowledgeable about a community or concern, is particularly beneficial in a study of community resilience to the extent that these informants are more accessible than a probability sample of the general population and can provide useful information. Affiliated volunteer responders, who constituted the sample for this study, should be valuable key informants because of their involvement in local disaster readiness and response and their knowledge of local hazards, organizations and systems, and populations. Although these groups frequently have defined roles during and after a disaster, professional responders do not always appreciate those roles or welcome the volunteers as part of a response effort. Affiliated volunteer responders’ perceptions of various aspects of community resilience may differ from the perceptions of other community members insofar as they may be better informed about emergency management, more attuned to underlying issues, more attached to their community, and/ or more committed to building disaster resilience than are other community members.

Associations Between Community Resilience Scores and Study Participant Characteristics Although significant associations alone do not indicate cause and effect, the associations found between various community resilience scores (domain scores, the total community resilience score, and the primary community resilience challenge) and study participant characteristics (including demographic and disaster-related characteristics) may be instructive for an understanding of community resilience and the role of affiliated volunteer responders in disaster management and resilience. It is not surprising that home ownership was associated with Connection and Caring, Resources, Transformative Potential, and the total community resilience score. The purchase of a house suggests commitment to, and investment in, a community (part of the Connection and Caring domain). Typically, people purchase homes where they expect to find support systems (part of the Resources domain) necessary to sustain their family. Home owners may be more inclined to communicate with leaders who can help improve the community and to work together with others to solve local problems and reach community goals (part of the Transformative Potential domain). This may lead to increased participation in community organizations and likely contributes to a sense of belonging (both part of the Connection and Caring domain). Participation in community organizations, such as affiliated volunteer responder groups, enhances community awareness and disaster response and contributes to community resilience. The ability to own a home may improve the capacity for preparedness and create a greater sense of security when an incident occurs, also contributing to community resilience.

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Active membership in an affiliated volunteer responder group was positively associated with the Connection and Caring and the Information and Communication domain scores. Active membership in a volunteer responder group suggests a level of awareness of, and commitment to, the community. It may be that decisions regarding participation in an affiliated volunteer responder group both influence and are influenced by a commitment to, engagement in, and a sense of belonging to one’s community as well as a perception that people in the community help each other (all part of the Connection and Caring domain). Active group members also are likely to have sources of useful information about disaster management and resilience (part of the Information and Communication domain) and the skills to interpret and utilize that information effectively. Access to relevant information, a sense of belonging and helpfulness among community members, and a commitment to, and engagement in, one’s community all advance community resilience, thus contributing to the total community resilience score. The negative relationships involving completion of basic training as part of membership in a volunteer responder group (which was negatively associated with Transformative Potential) and interest in being deployed within the community (which was negatively associated with both the total community resilience score and the primary challenge) are less obvious. Compared with volunteer responders who have not completed basic training, volunteer responders who have completed basic training may have a better appreciation of the complexity of issues associated with Transformative Potential such as the interpretation of community experiences, data collection and analysis, planning, and skill development. Thus, those who have completed basic training as part of membership in a volunteer responder group may score Transformative Potential lower than those who have not completed training. Lack of interest in being deployed within the community may be associated with higher community resilience scores and higher scores on trust in public officials if volunteer responders perceive less need for their deployment because they trust leadership and think the community can adapt without their involvement. These relationships, along with the relationships between Disaster Management and both sex and marital status, warrant further exploration in future studies. Participants who were interested in being deployed were less likely to agree that people in their community trust public officials (the primary community resilience challenge). One might expect the reverse to be true if one assumes that volunteer responders are motivated, in part, by a desire to contribute, which could be dampened by mistrust of public officials. Instead, it appears that interest in being deployed may be enhanced when trust is lacking, perhaps indicating that volunteer responders recognize the importance of their involvement in disaster management efforts. The finding that trust in public officials is the primary community challenge is troubling and warrants exploration beyond its implications for interest in deployment by affiliated volunteer responders. It is possible that affiliated volunteer responder groups could be used to build trust with the public if authorities are aware of the problem and choose to address it.

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The role of home ownership and active membership in an affiliated volunteer responder group as potential predictors of multiple community resilience domain scores and the total community resilience score in this sample suggests the importance of investment in one’s community both in terms of the motivations for investment and the benefits derived from it. There are implications here for encouraging investment and engagement of community members. It is unclear whether those investments need to be in a home, though home ownership certainly ties an individual to a community, and whether engagement needs to be in a volunteer responder group. Active participation in an affiliated volunteer responder group (rather than another group) appears here because the study sample involves affiliated volunteer responders. It seems likely that active participation in other significant community organizations and activities would yield similar findings.

Limitations The present CART survey application is not without limitations. First, participants were recruited on a voluntary basis, resulting in a sample that may not be representative of the affiliated volunteer responders in the geographic area surveyed or of affiliated volunteer responders in general. Also, the relatively small sample size may have prevented detection of some actual significant associations between community resilience scores and participant characteristics. Indeed, although some of the performed Wilcoxon tests reached the benchmark power of 0.80 to detect a large difference (n = 3, 30.0%), the majority did not (n = 7, 70.0%). Finally, though this study was not designed to permit conclusions about the relative value of using affiliated volunteer responders as key informants versus relying on other community samples, it is noted here that one cannot generalize from findings in the current study to other community samples.

Summary and Conclusions The CART Assessment Survey is a theory-based, evidence-informed instrument for assessing community resilience to disasters. Early applications of the survey identified four interrelated domains: Connection and Caring, Resources, Transformative Potential, and Disaster Management. Because of their importance in community resilience and disaster management, questions on information and communication were added to the expanded version of the instrument used in the current application. The addition of Information and Communication as a fifth domain gave rise to a five-factor model of community resilience. The application confirmed the five-factor model and the expanded survey instrument demonstrated good reliability when used with a sample of affiliated volunteer responders. Thus, it is recommended that the five-factor model and expanded CART Assessment Survey instrument be used in future CART assessment studies and by others interested in assessing community resilience. This study also demonstrated that affiliated volunteer responders can serve as key informants regarding community resilience. Survey findings for the study sample suggest the importance of home ownership and active, civic engagement (in the form of

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participation in affiliated volunteer responder groups) for community resilience. Although home ownership and civic engagement are perhaps obvious components of community commitment and involvement, the study results are a reminder that promoting such commitment and involvement may have importance beyond their intrinsic value, with implications for community resilience and disaster management as well. Acknowledgments The Communities Advancing Resilience Toolkit (CART) was developed by the Terrorism and Disaster Center (TDC), located at the University of Missouri (MU), Columbia, Missouri, USA, and at the University of Oklahoma Health Sciences Center (OUHSC), Oklahoma City, Oklahoma, USA. TDC is a partner in the National Child Traumatic Stress Network (NCTSN), which is funded by the Substance Abuse and Mental Health Services Administration (SAMHSA), U.S. Department of Health and Human Services (DHHS). Support for the development of CART also was provided by the National Consortium for the Study of Terrorism and Responses to Terrorism (START), located at the University of Maryland, College Park, Maryland, USA, which is funded by the U.S. Department of Homeland Security (DHS), and by the Centers for Disease Control and Prevention (CDC), DHHS. The findings, conclusions, opinions, and contents of this article are those of the authors and do not represent the official position of the CDC, DHHS, DHS, MU, NCTSN, OUHSC, SAMHSA, START, or TDC. This work would not have been possible without the involvement of survey participants and the support and collaboration of a state emergency management agency and leaders of affiliated volunteer responder groups.

Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Substance Abuse and Mental Health Services Administration 1U79 SM57278 and 1U79 SM061264.

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Author Biographies Rose L. Pfefferbaum, PhD, MPH, is a project director with the Terrorism and Disaster Center (TDC) of the National Child Traumatic Stress Network. She is responsible for TDC community resilience activities. Recently retired Professor of Economics and Director of Terrorism and Disaster Preparedness at Phoenix Community College, she has had extensive experience in community-based programs including work with community disaster response groups. Her PhD is in economics. Betty Pfefferbaum, MD, JD, is George Lynn Cross Research Professor in the Department of Psychiatry and Behavioral Sciences at the University of Oklahoma College of Medicine in Oklahoma City, Oklahoma. She is the Co-Director of the Terrorism and Disaster Center of the National Child Traumatic Stress Network. Her expertise is in child trauma and disaster mental health. She is a general and child psychiatrist and holds a law degree. Pascal Nitiéma, MD, MS, MPH, is a research biostatistician with the Terrorism and Disaster Center of the National Child Traumatic Stress Network. His expertise and areas of interest include psychometrics and the impact of mass trauma on child mental health. He has a medical background. His MS degree is in epidemiology. J. Brian Houston, PhD, is an assistant professor in the Department of Communication and is co-director of the Terrorism and Disaster Center at the University of Missouri. His research focuses on communication at all phases of disasters and on the mental health effects and sociopolitical consequences of community crises. His PhD is in communication. Richard L. Van Horn, PhD, is President Emeritus, Regent’s Professor Emeritus of Management Information Systems, Clarence E. Page Professor Emeritus of Aviation and Aerospace Studies, and Adjunct Professor in the Department of Psychiatry and Behavioral Sciences in the College of Medicine at the University of Oklahoma. His current work focuses on applying models and concepts from the management information system, management science, and strategic planning disciplines to understand and enhance community resilience. His PhD is in system sciences.