Int Arch Occup Environ Health (2011) 84:813–824 DOI 10.1007/s00420-011-0667-y
ORIGINAL ARTICLE
Exploring within- and between-gender differences in burnout: 8 different occupational groups Siw Tone Innstrand • Ellen Melbye Langballe Erik Falkum • Olaf Gjerløw Aasland
•
Received: 20 August 2010 / Accepted: 8 June 2011 / Published online: 18 June 2011 Ó Springer-Verlag 2011
Abstract Objectives The aim of this study was to examine gender differences in burnout within and between occupations using latent mean analysis. Methods Burnout was measured using the Oldenburg Burnout Inventory (OLBI), designed to assess the two sub-dimension exhaustion and disengagement. Men and women from eight different occupational groups in Norway were investigated: lawyers, physicians, nurses, teachers, church ministers, bus drivers and people working in advertising and information technology (n = 4,965). The The data collection was founded by the Research Institute of the Norwegian Medical Association. S. T. Innstrand (&) Research Centre for Health Promotion and Resources HiST/NTNU, Department of Social Work and Health Sciences, Norwegian University of Science and Technology, 7491 Trondheim, Norway e-mail:
[email protected] E. M. Langballe Division of Mental Health, Norwegian Institute of Public Health, Oslo, Norway E. Falkum Oslo University Hospital, Aker, Norway
average age was 42 years (SD 10.8), and 50.5% of the respondents were female. Within- and between-gender differences were examined by multi-group latent mean analysis by means of LISREL. Results Significant latent mean differences in the two dimensions of burnout between men and women were demonstrated. In general, the analyses indicate that overall, women report more exhaustion, but not more disengagement, than men. However, separate analyses indicate that the gender differences vary across occupational groups, especially for the disengagement dimension. Within-gender analyses suggest an approximately similar burnout profile across occupational groups for men and women. Conclusions Despite gender equality in society in general, and inconclusive findings in previous studies on gender differences in burnout, women in this study seem to experience slightly higher burnout levels than men. Occupational differences found in the burnout profiles indicate that some professions may be more prone to burnout than others. For the occupational groups most at risk, more research is needed to disclose potential organizational factors that may make these workers more prone to burnout than others. Keywords Burnout Gender differences Occupational differences Latent mean analysis Multi-group analysis
E. Falkum Institute of Clinical Medicine, University of Oslo, Oslo, Norway O. G. Aasland The Research Institute, Norwegian Medical Association, Oslo, Norway O. G. Aasland Department of Health Management and Health Economics, Institute of Health and Society, University of Oslo, Oslo, Norway
Introduction The academic interest in research on burnout started in the 1970s, triggered by Herbert Freudenberger’s descriptions of what he called ‘burn out’ among some of his colleagues working at a free healthcare clinic (Freudenberger 1974). These people changed from being motivated workers to
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gradually losing their commitment and energy, and becoming more and more frustrated and exhausted. There are many definitions of burnout, but the most often cited states that ‘‘burnout is a psychological syndrome of emotional exhaustion, depersonalization, and reduced personal accomplishment that can occur among individuals who work with other people in some capacity’’ (Maslach 1993, p. 20). In the early phases of research in particular, a central issue was how to understand the underlying dynamics of burnout. One of the early assumptions was that the emotional demands and the imbalance between the caregiver and the recipient in health services are the main contributors to the development of burnout (Cherniss 1980; Maslach 1988; Roness 1995; So¨derfeldt 1997). Recent studies, however, have demonstrated that general job stressors, such as workload, time pressure and role conflicts, may be just as important as person-related stressors. It is therefore argued that burnout can be a potential problem in all occupational sectors (Maslach et al. 2001; Toppinen-Tanner et al. 2002), and an important public health problem in modern work life (Shirom 2005). The relationship of burnout with gender is not clear cut (i.e. see Schaufeli and Enzmann 1998). Some studies report more burnout among women (Bakker et al. 2002; Dahlin et al. 2007; Hakanen 1999), whereas others suggest the opposite (Greenglass et al. 1990). A meta-analysis on burnout among special educators found the relationship between constructs of burnout and gender to be inconsistent (Edmonson 2000). In general, researchers have assumed that women would suffer from burnout more often than men because of women’s double workload and the inequality between sexes at work (Hakanen 1999). However, as times change, so do gender roles. In a comprehensive cross-national study on the work–life balance in Europe, Crompton and Lyonette (2006) found that Nordic countries (Norway and Finland) had more liberal genderrole attitudes and a less traditional division of labour compared with other European countries. Moreover, profound changes in the nature of work, as well as in family structures, have produced new responsibilities and new challenges for both men and women that potentially make both men and women more vulnerable to burnout. More specifically, it has been suggested that women tend to score slightly higher on emotional exhaustion, whereas men tend to report the most depersonalization as measured by Maslach’s traditional burnout measure (Maslach Burnout Inventory: MBI). A recent meta-analysis by Purvanova and Muros (2010) supported this assumption. These findings have partly been explained by gender differences in coping strategies (Greenglass et al. 1990) or sex role-dependent stereotypes in which women tend to be more emotionally responsive than men (Schaufeli and Enzmann 1998). In the present study, we explore gender
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differences in burnout as measured by the Oldenburg Burnout Inventory (OLBI) which assesses the two core dimensions of burnout, exhaustion and disengagement from work. In contrast to the MBI, which focuses exclusively on emotional aspects, the OLBI measures emotional, cognitive and physical components of exhaustion. This facilitates application of the instrument among workers who mainly perform physical work and among those who mainly process information. In a similar vein, disengagement in the OLBI refers to distancing oneself from one’s work in general, as well as from the work object and work content (e.g. perceiving work as uninteresting, no longer challenging and even ‘‘disgusting’’), and refers not only to distancing oneself emotionally from service recipients like the depersonalization in the original MBI (Demerouti and Bakker 2008). Based on previous findings and assumptions, one can expect gender differences in the experience of exhaustion and disengagement when measured by OLBI. Specifically, Hypothesis 1 Women experience higher levels of exhaustion than men. Hypothesis 2 Men experience higher levels of disengagement than women. Because most work sectors are segregated by gender, it is difficult to consider any gender differences in burnout without also discussing the roles of men and women in the society. Although there is a large increase in the proportion of managerial jobs that go to women, they still tend to be over-represented in the helping professions such as teaching, nursing and social work. In contrast, men are still the majority within physics, engineering and chemistry, while the gender breakdown in other job categories, such as medicine, law and journalism, approaches equality (Halpern and Murphy 2004). Evans and Steptoe (2002) found that men and women in jobs in which they are in a cultural and numerical minority may be especially vulnerable to stressrelated problems. A large survey of employees in Finland revealed that burnout is not only more prevalent among women, but also tends to vary by sector of employment and occupation (Hakanen 1999). Whereas the four industries showing the highest rate of exhaustion were typically dominated by women (i.e. education and research, banking and insurance, hotels and catering, and health care and social services), cynicism was most prevalent in male industries (i.e. machine repairs, transport and storage) and industries in which both sexes were equally represented. Similarly, normative data of the MBI based on 73 US studies published between 1979 and 1998 indicate that emotional exhaustion is most prevalent among teachers, social workers and in medicine, whereas particularly high levels of depersonalization were reported among the
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typically male-dominated professions like physicians and police officers (Schaufeli and Enzmann 1998). After controlling for the work sector, Demerouti et al. (2003) found no effect of gender on burnout as measured by OLBI. Thus, previous findings of gender differences or a lack of gender differences may have been due to the confounding of gender differences with occupation. In general, gender differences in burnout have not been interpreted unequivocally, because most studies have been carried out on human service professionals who spend most of their time interacting with people in some kind of distress. As most human service occupations are female dominated, gender differences may be confounded by type of occupation. Moreover, gender differences in burnout have mainly been measured by traditional statistical techniques like t tests or (M)ANOVA (i.e. Unterbrink et al. 2007), or they have been treated as a confounder. In the present study, gender differences in burnout are explored both within and between occupations using latent mean analysis. Since latent variables are not associated with measurement error, latent mean analysis is more sensitive than traditional statistical techniques and is more likely to detect between-group differences (Hancock et al. 2000; Hong et al. 2003). To the authors’ knowledge, this is the first attempt to explore gender differences in burnout by a latent mean analysis of different occupational groups. Specifically, Hypothesis 3 Gender differences in the experience of exhaustion and disengagement differ across occupational groups. Method Data collection Data in this study were collected in the first survey round of a two-wave panel study in Norway. The questionnaires
Table 1 Calculated weights for different occupational groups
were mailed to the employee’s home address with a onepage cover letter stating the purpose of the survey and ensuring confidentiality. Representative national samples of eight different occupational groups (lawyers, physicians, nurses, teachers, church ministers, bus drivers and people working in advertisement and information technology) were drawn by Statistics Norway. A description of each occupational group is presented below. Random samples of each occupation (n = 1,000) were drawn from the central Norwegian registers of employees and employment. Equal numbers of men and women were drawn from each occupation, thus reflecting the target populations equally well. The only exception was for church ministers, a population that contained 599 men and 401 women (because there were only 401 female church ministers in Norway at the time of the first data collection). It should be noted that some of these occupational groups are male or female dominated. For instance, there are more female than male nurses, and more males than females working in the IT industry in Norway. However, the male/female ratio in advertisement was almost equal at the time of the data collection. Table 1 shows the number of potential respondents which constituted the basis for the random sampling of potential respondents within each occupational group and gender in Norway at the time of the data collection. All respondents were offered participation in a small lottery for returning the questionnaire. Recent studies indicate that lottery incentives do not alter the selection of respondents (Aadahl and Jørgensen 2004). The response rate at Time 1 was 63%. The systematic evaluation of responses and attrition indicated that the sample is representative of the Norwegian populations in each occupation (Skaare 2006; Wedde et al. 2004). Moreover, inspection of the non-response patterns revealed that the average non-response rate was 36% in the first survey (Skaare 2006). Attrition rates between 30 and 40% are quite common (Taris 2000).
Population Male Lawyers Physicians Nurses Church ministers
Random samples of each occupation Female
Male
Female
Extracted weights
4,404
1,674
500
500
12,081
6,431
500
500
0.38 0.53
3,868
46,866
500
500
12.12
1,498
401
599
401
0.40
Bus drivers
12,161
907
500
500
0.07
Teachers
22,965
48,067
500
500
2.09
IT
15,937
4,240
500
500
0.27
1,393
1,143
500
500
0.82
Advertising
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Participants
Teachers
Advertisement
This occupational group (n = 676) consists of teachers working within the Norwegian school system, in both public and private schools, with students between 6 and 19 years of age (from the first grade through high school).
The advertising group (n = 505) consists of decorators, designers, art directors, distributors of commercial advertisement and others within the advertising industry.
Measure Bus drivers The sample included both bus and tram drivers (n = 579). They transported both passengers and cargo. Some of the drivers were also responsible for keeping the vehicle in good condition and for selling or controlling tickets. Ambulance personnel and long-distance drivers were not included. Church ministers This group mainly consists of church ministers, but it also includes some other respondents in clergy positions, such as catechists and missionaries (n = 679). Information technology workers This occupational group includes workers doing a wide variety of tasks such as programming, research, development of new data tools for administration, communication and information purposes, testing of data programs, designing and implementing new systems, user assistance, installation of new programs and the like (n = 589). Lawyers This sample includes people doing all kinds of work tasks related to the law, such as assisting private and business clients in court; designing contracts, business deals and wills; and giving legal advice in the bank, industry and insurance businesses (n = 580).
Burnout was measured by a Norwegian version of the 16-item Oldenburg Burnout Inventory (OLBI), translated by one of the authors, back-translated by a bilingual German psychiatrist and compared with the English and Swedish versions of the instruments. The construct and convergent validity of the measure have been confirmed in previous validation studies (Demerouti et al. 2001; Demerouti et al. 2003; Halbesleben and Demerouti 2005). The OLBI contains two burnout dimensions; exhaustion and disengagement from work (Halbesleben and Demerouti 2005). In the present Norwegian version, one of the items in the disengagement scale was changed. The original item ‘‘I always find new and interesting aspects in my work’’ can be perceived as mainly a measure of a general personality trait (i.e. positive thinker). To make it less ambiguous and more closely connected to the disengagement dimension of burnout, this was changed to ‘‘I am less interested in my job now than in the beginning.’’ Both the exhaustion and the disengagement sub-scales were described by eight items each. Sample items are ‘‘I feel emotionally depleted by work’’ (exhaustion) and ‘‘With time I have lost my deep interest in my job’’ (disengagement). The items were scored on a 5-point scale (1 = totally disagree, 5 = totally agree). The internal consistencies of the variables were satisfactory for exhaustion (men: a C .86, women: a C .87) and disengagement (men: a C .87, women: a C .87). Working hours were assessed by self-reported hours worked during an average week. Data analysis
Nurses This group includes ordinary nurses, midwives and nurses with some sort of specialization. The tasks include treatment and the caring and guidance of sick or wounded individuals (n = 681). Physicians This sub-sample includes public and private practitioners (specialists and non-specialists) doing clinical, administrative, or scientific work within the medical field (n = 676).
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Gender differences were examined using a multi-group latent mean analysis (LISREL 8.72: Jo¨reskog and So¨rbom 2004) with maximum likelihood estimations, as preliminary analyses of the distribution of the data indicated no serious departures from normality (West et al. 1995). Missing values were treated listwise. Due to the sensitivity of sample size in Chi-square statistics (Diamatopoulos and Siguaw 2000; Hair et al. 1998; Hu and Bentler 1995; Sharma 1996), the Root Mean Square Error of Approximation (RMSEA), the Non-Normed Fit Index (NNFI) and the Comparative Fit Index (CFI) were used as additional measures of fit. By convention, there is a good model fit if
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the RMSEA is less than or equal to .05, and there is an adequate fit if the RMSEA is less than or equal to .08. The NNFI and CFI should be equal to or greater than .90 in order for the model to be accepted (Diamatopoulos and Siguaw 2000). Gender differences were examined in two steps. First, before testing the between-group differences, we examined a series of measurement invariance tests. Meaningful comparisons can only be made if the measure is comparable across different groups (Chen 2008). Thus, the measurement structure has to be equivalent (invariant), albeit not perfect (Byrne et al. 1989). To allow the model to be fitted to men and women simultaneously, a different invariance constraint test was performed using multi-group covariance structure latent means, following the procedure suggested by Bollen (1989) and Millsap and Everson (1991). Testing for invariance involves specifying a model in which certain parameters are free to take any value across groups (the variant model), and then compare that model with the more restrictive case in which these parameters are constrained to be equal across groups (the invariant model). If the difference in fit (Dv2) is not significant, the hypothesis of equal parameter estimates across multiple samples is considered to be tenable. However, given the limitations of the v2-statistics with large sample sizes, and in line with the recommendations set forth in the literature (Cheung and Rensvold 2002; Steenkamp and Baumgartner 1998; Vandenberg and Lance 2000), we examined the change in CFI along with other indices of practical fit; including the RMSEA and the NNFI. As a rule of thumb, it has been recommended that changes in CFI should not exceed -.02. Nevertheless, as the literature on the critical values for the change in CFI is rather new, it is recommended that the CFI criterion only be used as a supplement (Vandenberg and Lance 2000). Consequently, no restrictions were specified in the first model (configural invariance). In the second model, the factor loadings were set to be invariant across the different samples (metric invariance); whereas the indicator intercepts were set to be invariant in the third model (scalar invariance). Finally, both factor loadings and indicator intercepts were set to be invariant across all samples (metric and scalar invariance). Second, to study gender differences in burnout, the latent mean (kappa) in the final model (both factor loadings and intercepts equate) was examined. Constraining invariant factor loadings and intercepts across the groups simplifies the interpretation of mean differences (Millsap and Everson 1991). Since the two dimensions of burnout are conceptualized as latent constructs that cannot be measured directly, traditional approaches to assessing between-group differences (i.e. t test, MANOVA) can yield misleading results, as they are based on the scores of measured or composed variables which are subject to
817
measurement error. In contrast, the latent mean analysis tests the latent construct of interest and is not associated with measurement error. Hence, latent mean analysis is more sensitive and accurate than traditional statistical techniques, and more likely to detect differences between groups (Hong et al. 2003; Greenglass et al. 1990). Testing for latent mean differences requires that the factor loadings and intercepts are constrained to be equal across groups. Because it is not possible to define an origin for the latent variable, the latent mean is fixed to zero in one group (i.e. reference group) and estimated in the other group (i.e. comparison group). In line with Hardy’s guidelines (1993) for choosing a reference group in the upper or lower boundary to ease interpretation, the men were used as a reference group in the within-occupation analyses and the lawyers were used as reference group in the betweenoccupation analyses. Figure 1 presents the path diagrams for the latent means analysis. Representation of intercepts of factors and observed variables were achieved by specifying a constant value of unity shown in a triangle, with its influence on each of the factors and observed variables (i.e. see Hong et al. 2003). In Fig. 1, the values of b1 and b2 were constrained to be zero for the references group (male group and lawyers), whereas these values were estimated for the female group and the other occupational groups. Thus, the test of differences in latent means was based on the significance of the parameter estimate in the comparison groups. First, gender differences were examined in the total sample. Then, separate analyses were done for each occupation to examine whether the gender differences were alike across occupational groups (between-gender analyses). Finally, occupational differences in burnout were
Fig. 1 Path diagram for latent mean analysis. The latent variables, exhaustion and disengagement, were indicated by eight measured items each, as illustrated by the squares in the model (exhaustion: y1 to y8) and (disengagement: y9 to y16). The triangle in the figure represents the Unit Constant which signifies the intercept-constant nature. Its mean, or intercept, was estimated by each indicator being regressed on the Unit Constant
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123 Mean (95% CI) 36.1
Mean (95% CI)
42.2
(40.8–43.5)
46.7
(45.5–47.9)
38.9
(37.8–40.0)
48.0
(46.8–49.2)
46.4
(45.2–47.6)
49.4
(48.4–50.4)
39.5 (38.1–40.9)
39.9
(38.9–40.9)
44.3
(43.8–44.7)
Lawyers
(nm = 269/nw = 304)
Bus drivers
(nm = 282/nw = 288)
IT
(nm = 281/nw = 302)
Physicians
(nm = 308/nw = 356)
Teachers
(nm = 335/nw = 339)
Church Ministers
(nm = 406/nw = 265)
Advertising (nm = 225/nw = 277)
Nurses
(nm = 338/nw = 335)
Total
(nm = 2,444/nW = 2,466) (42.0–42.8)
42.4
(36.2–37.6)
36.9
43.1 (41.7–44.4)
(43.7–45.4)
44.5
(38.5–40.6)
39.5
(47.5–49.4)
48.5
(41.3–43.4)
42.3
(36.6–39.1)
37.9
(45.4–47.6)
46.5
(38.8–39.6)
39.2
(31.9–33.7)
32.8
39.1 (38.5–40.8)
(38.5–40.8)
39.6
(36.0–38.3)
37.2
(43.7–45.3)
44.5
(38.6–40.4)
39.5
(33.0–35.5)
34.2
(43.8–45.6)
44.7
Mean (95% CI)
Women
(2.6–2.7)
2.6
(2.6–2.7)
2.6
2.7 (2.5–2.8)
(2.6–2.7)
2.7
(2.7–2.9)
2.8
(2.6–2.8)
2.7
(2.5–2.7)
2.6
(2.6–2.8)
2.7
(2.4–2.6)
2.5
Mean (95% CI)
Men
Exhaustion
95% CI 95% Confidence intervals, nm number of men, nw number of women, IT Information technology
(40.0–40.8)
40.4
(41.3–43.6)
42.5
36.1 (35.1–37.1)
(40.0–42.3)
41.2
(43.9–46.2)
45.0
(40.8–42.9)
41.8
(36.9–38.8)
37.8
(40.5–42.8)
41.7
(35.2–37.1)
Men
Women
Men Mean (95% CI)
Work hours
Age
Occupation
Table 2 Descriptive statistics by gender and occupation
4
2.6
4
2.6
2.5 4
4
2.6
4
2.8
4
2.6
4
2.5
4
2.6
3
2.5
Median range
(2.8–2.8)
2.8
(2.7–2.8)
2.8
2.8 (2.7–2.9)
(2.8–3.0)
2.9
(2.9–3.1)
3.0
(2.7–2.9)
2.8
(2.6–2.8)
2.7
(2.7–2.9)
2.8
(2.6–2.7)
2.7
Mean (95% CI)
Women
4
2.8
4
2.8
2.8 4
4
2.9
4
3.0
4
2.8
4
2.6
4
2.9
3
2.6
Median range
(2.3–2.3)
2.3
(2.4–2.5)
2.5
2.4 (2.3–2.6)
(1.9–2.0)
1.9
(2.3–2.5)
2.4
(2.1–2.2)
2.1
(2.4–2.5)
2.4
(2.5–2.7)
2.6
(2.0–2.2)
2.1
Mean (95% CI)
Men
4
2.3
4
2.4
2.3 4
3
1.9
4
2.3
3
2.1
4
2.4
4
2.6
3
2.0
Median range
Disengagement
(2.3–2.3)
2.3
(2.1–2.2)
2.1
2.6 (2.5–2.7)
(2.0–2.1)
2.0
(2.1–2.3)
2.2
(2.0–2.1)
2.0
(2.5–2.7)
2.6
(2.4–2.6)
2.5
(2.1–2.3)
2.2
Mean (95% CI)
Women
4
2.1
4
2.0
2.6 4
3
2.0
4
2.1
4
2.0
4
2.5
4
2.4
3
2.1
Median range
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explored within each gender separately (within-gender analyses). The magnitude of the significant latent mean differences was tested by Cohen’s d effect size calculation (Cohen 1988) and evaluated by following his guidelines (d = .20, .50 and .80 for small, medium and large effects, respectively). The d index indicates the difference between the means of the two groups divided by the pooled standard deviation across groups and can be used when the variance of the two groups are homogeneous. When the assumption of homogeneity of variance across groups is met (i.e. variance of exhaustion and disengagement across gender), the common standard deviation can be used (Hong et al. 2003).
Results Table 2 displays the descriptive statistics (age, work hours, exhaustion and disengagement) by gender and occupation computed by SPSS. Overall, the male church ministers were the oldest (49.4 year), and the female lawyers and the female working in advertising were the youngest (36.1 year). The male physicians reported the most average working hours per week (48.5), when compared to the Norwegian standard of 37.5 for full time workers. The female nurses reported the least working hour per week (32.8). Tests of measurement invariance across gender Table 3 displays the fit indices for the models that tested measurement invariance. The configural model provided an acceptable fit to the data, with NNFI and CFI greater than .90 (NNFI = .96; CFI = .96) and RMSEA close to .08 (RMSEA = .088). These results indicate that men and women agree on the number of burnout dimensions and on the particular items associated with each dimension. The hypothesis of metric invariance was tested by constraining the matrix of factor loadings to be invariant across gender. The chi-square increase was not significant (Dv2 (14) = 13.86, p [ .05), indicating that the men and women agree
819
on the relative importance of the items as indicators of the latent construct. Scalar invariance (i.e. equal item intercept) was partly demonstrated as the CFI did not change and the change in RMSEA was less than .015 (Chen 2007). Consequently, between-group differences in latent means could be calculated (Chen 2008; Cheung and Rensvold 2002; Greenglass et al. 1990), and a model with both factor loadings and intercepts equated was performed (RMSEA = .085; NNFI = .96; CFI = .96). Finally, in order to test the magnitude of the significant latent mean differences using Cohen’s d effect size (Cohen 1988), the assumption of homogeneity of variance across group was tested. As there was no change in the RMSEA, NNFI and CFI when comparing model 4 and 5, the homogeneity of variance assumption was not rejected, and the effect size was computed using common standard deviations. The effect size (d) is presented in parentheses behind each significant latent mean parameter in Tables 4 and 5. Test for latent mean differences Between-gender analyses Multi-group analyses of the total sample (v2 (234) = 3,989.28, p \ 0.001, RMSEA: 0.084, NNFI/CFI: 0.96) showed significantly higher latent mean exhaustion among women than men (kappa: 0.17, p \ .05), but no gender differences in disengagement. As can be seen in Table 4, separate latent mean analyses on each occupational group indicate that gender differences in exhaustion is true for most occupations. Except for the females working within Information Technology (IT) and advertising, all women reported more exhaustion when compared to their male counterparts. However, gender differences in disengagement were not that clear cut. Whereas the female physicians, teachers and nurses were less disengaged than their male colleagues, the female church ministers and those working in advertising reported more disengagement than their male counterparts. There were no gender differences in disengagement among the lawyers, bus drivers, or among those working with IT.
Table 3 Test for measurement invariance across gender (n = 4,965) v2
df
RMSEA (90% CI)
NNFI
CFI
M1. No common parameters
3,828.50
206
.088 (.086–.091)
.96
.96
M2. Invariant factor loadings
3,842.36
220
.086 (.083–.088)
.96
.96
M3. Invariant indicator intercepts
4,079.40
222
.088 (.085–.090)
.96
.96
M4. Invariant factor loadings and indicator intercepts
4,093.14
236
.085 (.083–.088)
.96
.96
M5. Invariant factor loadings, indicator intercepts and factor variance
4,114.82
239
.085 (.083–.087)
.96
.96
2
v Chi-square, df degrees of freedom, RMSEA root mean square error of approximation, NNF non-normed fit index, CFI comparative fit index, M Model
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Table 4 Separate latent means structure analysis by occupation and fit indices Occupationa
v2
Kappa Exhaustion
df
RMSEA
CFI
NNFI
Disengagement
Lawyers
0.18* (0.18)
0.08
787.51
234
0.096
0.94
0.94
Bus drivers
0.16* (0.13)
-0.05
667.41
234
0.086
0.96
0.96
IT
0.13
0.16
653.71
234
0.081
0.96
0.96
Physicians
0.13* (0.10)
-0.22* (-0.17)
838.24
234
0.091
0.95
0.95
Teachers
0.22* (0.18)
-0.27* (-0.19)
742.38
234
0.083
0.96
0.96
Church ministers
0.27* (0.25)
0.15* (0.14)
717.62
234
0.081
0.95
0.95
Advertising Nurses
0.13 0.16* (0.13)
0.25* (0.14) -0.31* (-0.20)
774.43 710.54
234 234
0.10 0.082
0.95 0.96
0.95 0.96
Comparing gender differences in the latent mean levels of the two burnout dimensions for each occupation separately * Statistical significance (at p \ 0.05 or better) determined by the t value in excess of 1.96 of the latent mean a
Parameter estimates for women. The latent mean values for men were set to zero (reference group). Kappa is the latent mean of each factor generated by LISREL. Cohen’s d effect size (d) in parentheses
Table 5 Separate latent means structure analysis by gender. Comparing the latent mean levels of the two burnout dimensions across occupation separately for men and women Occupation
Men
Women
Kappa
Kappa
Exhaustion
Disengagement
Exhaustion
Disengagement
Lawyers
0
0
0
0
Bus drivers
0.21* (0.19)
0.57* (0.36)
0.20* (0.16)
0.45* (0.29)
IT
0.13
0.42* (0.29)
0.07
Physicians
0.21* (0.18)
0.06
0.15* (0.12)
Teachers
0.33* (0.27)
0.34* (0.21)
0.38* (0.31)
Church ministers Advertising
0.18* (0.15) 0.23* (0.20)
-0.27* (-0.17) 0.42* (0.27)
0.27* (0.25) 0.17* (0.13)
Nurses
0.17* (0.14)
0.41* (0.26)
0.14* (0.12)
0.50* (0.33) -0.23* (-0.16) 0.00 -0.22* (-0.18) 0.59* (0.37) 0.03
The latent mean values for lawyers were set to zero (reference group). Kappa is the latent mean of each factor generated by LISREL. Cohen’s d effect size (d) in parentheses * Statistical significance (at p \ 0.05 or better) determined by the t value well in excess of 1.96 of the latent mean
The computed values of d were all defined as small, based on Cohen’s (1988) guidelines. Within-gender analyses Table 5 shows the latent mean levels of the two burnout dimensions across occupation separately for men and women. Among both the male and female samples, the lawyers reported the least exhaustion, whereas the teachers reported the most. Moreover, male and female church ministers report the least disengagement, together with female physicians. Male bus drivers and female advertising professionals report the most disengagement. However, the computed values of Cohen’s d were all small. Figure 1 visualizes the burnout profiles from Table 5 for each of the eight occupations separately for men and women. It should
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be noted that Fig. 1 comprises four separate analyses, with lawyers as the reference group with scores set to zero within its respective gender group. Positive values indicate that scores on that particular dimension are higher than the score of the reference group (lawyers) and negative scores indicate that scores are lower. Significant differences are highlighted in bold. Despite small differences, Fig. 2 indicates that the profiles of the two burnout dimensions are quite similar for men and women within the same occupational groups.
Discussion This study investigated within- and between-gender differences in burnout, as measured by the Oldenburg Burnout
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Fig. 2 Profile of burnout for the occupational group by gender. The latent mean values for lawyers were set to zero (reference group). Significant differences from this reference group is highlighted in bold
Inventory (OLBI) which assesses the two core dimensions of burnout—exhaustion and disengagement from work. Tests for factorial invariance supported the use of the OLBI scale and that latent means were comparable between men and women in the total sample. A significant latent mean difference between men and women in perceived burnout was demonstrated. In general, women reported significantly more exhaustion, but not more disengagement, than did men. This is in accordance with previous findings (i.e. Maslach et al. 1996) suggesting that females score higher than males on exhaustion, but not on negative attitudes (depersonalization). The higher level of exhaustion among women is in line with the assumption that women are more vulnerable to burnout (and especially exhaustion) because of women’s double workload and inequality between the sexes at work (Hakanen 1999). Even though the gap between the genders in Norway is among the smallest in the world when it comes to economic participation and opportunities, educational attainments and political empowerment (Hausmann et al. 2008), recent findings indicate that there is a gap between principles and practice when it comes to equal opportunities for combining employment and parenthood (Bø 2008). Women are found to be at particular risk of work–family conflict (Innstrand et al. 2009), which has been strongly and reciprocally associated with the development of exhaustion (Innstrand et al. 2008). However, the separate
analyses in each occupational group indicate that gender differences in burnout are largely dependent on profession (Table 4). Although women in most occupations report more exhaustion than their male counterparts (exception: IT and advertising), thus supporting Hypothesis 1, the picture for disengagement is slightly different. Whereas the female church ministers and those working with advertising reported more disengagement than their male colleagues, the female physicians, teachers and nurses reported less disengagement when compared to the men in their profession. Similar findings have been reported previously among teachers (Unterbrink et al. 2007), school personnel (Greenglass et al. 1990) and dentists (te Brake et al. 2003). No gender differences were found in disengagement among the lawyers, bus drivers and people working with IT. Thus, Hypothesis 2, which suggests more depersonalization among men when compared to women, was only partly supported. Instead, the present study indicates that the inconsistency in previous findings regarding gender differences in burnout might be due to occupational differences. Although gender differences in the experience of burnout seem to vary across occupational groups, supporting Hypothesis 3, the within-gender analyses indicate quite similar occupational burnout profiles for both men and women (see Table 5; Fig. 2). The most exhaustion was reported among the teachers, advertising professionals, bus
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drivers, physicians and church ministers, for both men and women. Similarly, both men and women report the lowest mean exhaustion levels within nursing, IT and law. Both men and women working as bus drivers, in IT or in advertising reported the most disengagement. The lowest mean disengagement levels were reported among the male and female physicians, lawyers and church ministers. The high levels of exhaustion among the male and female teachers are in line with the findings of Maslach et al. (1996) as reported in the MBI manual. However, while previous studies show that physicians and police officers report the highest levels of depersonalization (Maslach et al. 1996 in Schaufeli and Enzmann 1998), our study suggests that both male and female physicians are among those who report the least disengagement (see Table 5). In fact, in contrast to the MBI manual (Maslach et al. 1996 in Schaufeli and Enzmann 1998), our study suggests less disengagement among the more highly trained professions (church ministers, physicians and lawyers) when compared to the less trained (i.e. bus drivers). These contradictory findings may be due to different construct operationalizations. Whereas higher scores on depersonalization among men in the original MBI (e.g. becoming impersonal, callous, hardened) have usually been explained as a result of the shaping power and prescription of the masculine gender role (Greenglass et al. 1990), this effect might not be evident in cynicism, as suggested by Bakker et al. (2002), nor disengagement, as used in our study. Just as cynicism refers to a lack of interest in the job and job meaningfulness, disengagement in the OLBI refers to distancing oneself from one’s work in general, work object and work content (e.g. uninteresting, no longer challenging, but also ‘‘disgusting’’) (Demerouti and Bakker 2008). This general distancing from work, and finding the job uninteresting, may not be particularly gender specific. Moreover, the disengagement items concern the relationship between employees and their jobs, particularly with respect to identification with work and willingness to continue in the same occupation (Demerouti and Bakker 2008). A stronger identification might be found among the more highly trained professions like the church ministers, physicians and lawyers. For example, the vocational calling often underlying the choice of profession as a church minister indicates a life-long career and a greater deal of religious identity salience (i.e. the importance of a person’s religious identity relative to that person’s other identities; Wimberley 1989). In that respect, the lesser degree of disengagement found among the highly trained professions in our study might not be so unexpected. Nevertheless, segregated occupational recruitment of persons with different personalities and different traits could possibly also be part of the explanation for the different reporting of exhaustion and disengagement across different occupations. For
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example, a recent study by Polman et al. (2010) suggests that the relationship between perceived stress and disengagement as measured by OLBI is moderated by Type D personality. Future studies should examine whether or not the combination of personal and organizational factors makes some workers more prone or resilient to burnout than others. It should be noted that that the aim of this study was to explore gender differences in the experience of burnout among eight different occupational groups in Norway and not their level of burnout per se. In most countries, burnout is not an approved clinical diagnosis, and it is not a diagnosis according to any of the international diagnostic manuals (ICD-10 or DSM-IV). Hence, no clinically validated cut-off points that can discriminate burned out cases from non-burned out cases exists (Dahlin et al. 2007; Schaufeli and Enzmann 1998). Yet, inspection of the total average burnout values in Table 2 indicate that the present sample does not differ notably from similar studies (i.e. Leiter and Maslach 2004; Demerouti and Bakker 2008). In general, most burnout research suffers from the healthy worker effect, a systematic bias because only working— and thus relative healthy—employees are studied. This is also likely in the present study, providing an underestimation of the true number of burnout cases. Nevertheless, as long as burnout is regarded as a major health problem and the end-state of a process, the identification of different burnout profiles among men and women is important in order to stop this process before it gains momentum.
Strength, limitations and future research Although tests of latent mean invariance are methodologically superior to traditional tests that simply assume metric and scalar invariance, the approach used here has some limitations. First, like other simple mean comparisons or zero-order relationships, tests of mean invariance across groups cannot rule out the possibility of spurious relationships or unmeasured third variables (Steinmetz et al. 2007). Available data suggest that there might be gender differences in the antecedents of burnout. Whereas work stressors are more related to symptoms of psychological distress among men, the interrelationship between work and family variables is significant contributors of burnout in women (Greenglass 1991; Langballe et al. 2010). Future studies should look for social gender-related practices and structures both at work and outside work, which are often very different for men and women, and explore how these relate to burnout. For example, could the gender wage gap have had any impact on the measured dimensions of burnout? Nevertheless, although the present study and the
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reported findings in the literature in general are presented and discussed by a gender-role theoretical framework, biological differences are also plausible. Secondly, although strong tests for factorial invariance supported the use of the OLBI scale, and the fact that it was comparable for men and women, the RMSEA was rather weak in some of the separate analyses performed on each occupation. The present findings have to be interpreted with this in mind. Replicate studies of the OLBI scale performed on several occupational groups are highly warranted and needed before any generalizations can be made. Thirdly, albeit significantly, most of the gender differences found are defined as small, based on Cohen’s (1988) guidelines. However, in practice, such differences may be more meaningful than statistics suggest. In a meta-analysis on gender differences in burnout, Purvanova and Muros (2010) demonstrated how even small differences have major implications when the effect sizes are accounted into per cent and multiplied with the actual number of workers affected by burnout. It should be noted that gender differences in burnout were tested by latent mean analysis in the present study. Since latent variables are not associated with measurement error, latent mean analysis is more sensitive than traditional statistical techniques and is more likely to detect between-group differences (Hancock et al. 2000; Hong et al. 2003). Thus, albeit small, the significant group differences found may be more valid. Nevertheless, it should be noted that the present study was conducted in Norway, which has generally been regarded as one of the most progressive countries when it comes to gender-role equality (Hausmann et al. 2008). Norway has an array of statutory rights such as the right to child care in kindergarten and other welfare services for those who need it, paid leave (maternity, paternity and parental leave, leave for the care of sick children, leave for the care of young children), and working time arrangements, including flexible working hours. Thus, the gender differences found in the present study may be even stronger in countries where labour policies are more conservative, as suggested by Purvanova and Muros (2010). Finally, all of the variables in the present study were measured using self-reporting instruments. Such measures may have introduced common method variance, inflating the relationship among the study variables. Moreover, the cross-sectional design indicates that no cause-or-effect conclusions can be drawn. The data only display associations between gender/occupation and self-reported burnout. Despite these limitations, it is important to examine gender differences as a first stage in this research direction. The strength of this study is the use of latent mean analysis and the large representative samples of men and women from eight different occupational groups in Norway. The inclusion of the OLBI measure allows for comparison of
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gender differences across several occupational groups. Whereas the MBI was primarily constructed to be used among human service professionals, the OLBI seems a better fit for studies of workers who also perform physical work or those whose job mainly has to do with processing information. Knowledge of how men and women in different occupational groups differ from one another in their experience of burnout is important for researchers, work organizations and clinicians. Visualization of how some occupations cluster or differ from each other may engender further causal studies and theory building, thus increasing the understanding of the risk and resilience factors of burnout. Practically, it can lead to the identification of those in need for prevention or intervention (i.e. for work organizations) and to the elimination of health-related or psychosocial factors associated with burnout (i.e. for clinicians). Conflict of interest of interest.
The authors declare that they have no conflict
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