The Mediating Role of Mental Health in the Relationship Between Job Stress and Job Satisfaction Among Bangladeshi Police Personnel

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Shakhawat Hossain This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9097729/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Police personnel face unique occupational challenges that place them at elevated risk for stress-related difficulties. This study examined whether mental health mediates the relationship between job stress and job satisfaction among police personnel in Bangladesh. Data were collected from 374 officers of the Chattogram Metropolitan Police. Correlation analyses indicated that job stress was negatively associated with mental health (r = –.52, p < .001) and job satisfaction (r = –.69, p < .001), whereas mental health was positively associated with job satisfaction (r = .58, p < .001). Hierarchical regression analyses controlling for demographic variables showed that job stress was a strong negative predictor of job satisfaction (β = –.65, p < .001), and mental health was a significant positive predictor (β = .35, p < .001). Mediation analysis using the PROCESS macro with 5,000 bootstrap samples further indicated that job stress significantly predicted poorer mental health (B = − 0.14, p < .001), and mental health significantly predicted job satisfaction (B = 0.92, p < .001). The indirect effect of job stress on job satisfaction through mental health was significant (B = − 0.13, 95% CI [–0.153, − 0.102]), indicating partial mediation. These findings suggest that the relationship between job stress and job satisfaction operates both directly and indirectly through mental health. Interventions targeting stress reduction and mental health support may therefore contribute to improved job satisfaction among police personnel. job stress mental health job satisfaction police personnel Figures Figure 1 Introduction Police work is widely recognized as one of the most demanding and stressful occupations in the public sector. Officers routinely face life-threatening and traumatic incidents as well as heavy bureaucratic workloads, leading to elevated rates of mental health problems compared to the general population. For example, exposure to human suffering and danger is related to increase risks of PTSD, depression, and suicidal ideation among police officers (Violanti et al., 2017; Cao et al., 2022). These findings underscore that police stressor—both operational (e.g., violence, accidents) and organizational (e.g., long hours, role conflict, inadequate support)—can significantly degrade officers’ well-being. Mental health is a crucial dimension of well-being. The World Health Organization defines it as a state of well-being that enables people to cope with life’s stresses and function effectively (WHO, 2024). Poor mental health (e.g., anxiety, depression) in turn predicts burnout and reduced quality of life (Violanti et al., 2017; Cao et al., 2022). In policing, evidence shows that high job demands and chronic stress are strongly associated with anxiety and depression symptoms (Queirós et al., 2020; Clays et al., 2007). Given this link, an officer under sustained stress is likely to experience impaired psychological health, in line with Lazarus and Folkman’s transactional model of stress and coping (1984), which posits that individuals’ appraisals of stressors affect health outcomes. Notably, the Job Demands–Resources model (Demerouti et al., 2001) also predicts that excessive demands without adequate resources (e.g., support, autonomy) lead to strain and adverse health outcomes. Job satisfaction reflects a worker’s positive appraisal of one’s job, including factors like pay, support, work conditions, and intrinsic meaning (Locke, 1976; Spector, 1997). It is a key predictor of employee well-being and organizational performance. High satisfaction typically relates to higher morale and retention, whereas dissatisfaction correlates with turnover intentions and poor productivity (Judge et al., 2001; Fernández-Salinero et al., 2026). Research in policing indicates that officers’ satisfaction depends heavily on their work environment: supportive supervision, fairness, and recognition enhance satisfaction, whereas role conflict, lack of resources, and work–life imbalance reduce it (Anders et al., 2024). Indeed, Anders et al. (2024) found that organizational justice, decision latitude and social support were among the strongest protective factors for police well-being and job satisfaction. Empirically, higher occupational stress tends to coincide with lower job satisfaction. For example, in a large longitudinal U.S. study, police officers’ stress negatively predicted their later job satisfaction (Mumford et al., 2025). Similarly, healthcare workers and other employees consistently report that high stress and burnout correlate with reduced job satisfaction (Melnyk et al., 2013). Theoretically, high demands and low control undermine satisfaction (Karasek, 1979), whereas resources and coping bolster it (Demerouti et al., 2001). Positive psychological health fosters engagement and satisfaction of work, while poor mental health undermines one’s perception of work. Recent studies confirm a robust positive correlation between mental well-being and job satisfaction (Cao et al., 2022). In particular, they found that individuals with better mental health reported significantly higher job satisfaction, even after accounting for contextual factors. This suggests that mental health may be a key intervening variable: if stress impairs mental health, and mental health in turn shapes job attitudes, then mental health could mediate the stress–satisfaction relationship. Despite these connections, few studies have formally tested mental health as a mediator between job stress and satisfaction in police samples. One relevant study (Nelson & Smith, 2016) noted that job stress works as a mediator between police officers’ work conditions and their well-being. Hence, understanding whether officers’ mental health explains the link between occupational stress and satisfaction can inform targeted interventions. If mental health is a key pathway, then strengthening psychological resources could buffer the adverse impact of job stress. Guided by the above literature, we posit the following hypotheses: H1: Higher job stress will be associated with poorer mental health among police employees. H2: Better mental health will be positively associated with higher job satisfaction. H3: Higher job stress will be associated with lower job satisfaction. H4: Mental health will mediate the relationship between job stress and job satisfaction, such that stress leads to poorer mental health, which in turn leads to lower satisfaction. These hypotheses align with theoretical models of stress (Lazarus & Folkman, 1984) and resources (Demerouti et al., 2001) and with empirical findings that stressors affect psychological health which subsequently influences broader job attitudes. [Insert Figure 1 about here] Method Participants The study sample comprised of 374 police employees from the Chattogram Metropolitan Police (CMP), which includes 16 Police station of Chittagong district, Bangladesh. All respondents are from different parts of Bangladesh, working at the CMP at the time of data collection. A non-probability sampling method was used for data collection. All respondents are between the ages of 25 and 48. Sample size was determined using Yamane's formula (1967): n = N/(1 + N(e)²), where N = 5,785 and e = 0.05, yielding the final sample of 374. Procedure Following institutional approval and participants’ informed consent, questionnaires were distributed to CMP police personnel. The questionnaire packet included demographic items and standardized measures of job stress, mental health, and job satisfaction. Questionnaires were administered individually. Assessments and Measures Bangla version of the Occupational Stress Index (OSI) . Job stress was measured using the Occupational Stress Index developed by Srivastava and Singh ( 1981 ). The scale consists of 46 items with 5 response categories (strongly disagree to strongly agree). Of the 46 items, 28 are true-keyed and 18 are false-keyed. The possible range of scores is 46 to 230, with higher scores indicating higher levels of stress. For the present study, the Bangla version adapted by Sorcar and Rahman ( 1989 ) was used, consists of 46 items with 5 response categories (strongly disagree to strongly agree). In the present study, Cronbach’s α was ( α = .81). Bangla version of the General Health Questionnaire (GHQ). The Bangla version of the General Health Questionnaire (GHQ-12), adapted by Sorcar and Rahman ( 1989 ), was used to measure the mental health of the participants. GHQ-12, originally developed by Goldberg ( 1972 ), is designed to detect minor psychiatric disorders in community and primary healthcare settings. The 12-item GHQ was derived from 60 items from the original version. Sorcar and Rahman ( 1989 ) adapted a new Likert-type scoring system in which true-keyed items (all positively worded items) of their questionnaire, with scores of 0, 1, 2, and 3, were assigned ‘not at all’, ‘somewhat’, ‘to a considerable extent’, and ‘to a great extent’ respectively. The scoring for the false-keyed items was reversed. The higher the score, the better the mental health. In the present study, Cronbach’s α was ( α = .70). Bangla version of Job Satisfaction Scale (JSS). The Bangla version of the job satisfaction (JSS) scale (Rahman, 2003 ) was originally developed by Warr et al. ( 1979 ). The JSS scale contains 15 statements on different aspects of the job. It is a seven-point Likert-type scale. Response options range from ‘I am not at all satisfied’ to ‘I am quite satisfied’. Scores from 1 to 7 are assigned accordingly. The highest and lowest possible total scores are 105 and 15, respectively, with higher scores indicating greater job satisfaction. In the present study, Cronbach’s α was ( α = .86). Results A total of 374 police officers from the Chattogram Metropolitan Police (CMP) in Bangladesh participated in this study. Data analysis was conducted using SPSS (Version 26) and the PROCESS macro for SPSS (Hayes, 2017 ). Participant Characteristics Table 1 presents the demographic characteristics of the sample. Regarding age distribution, the largest group of participants was aged 37–42 years (45.5%). The sample was predominantly male (86.1%), with female officers accounting for 13.9% of participants. In terms of educational attainment, the majority of police employees held a Master's degree (77%). Monthly income varied across participants, with the largest proportion (35.3%) earning between 26,000 and 35,000 BDT. The majority of participants were married (90.9%). Regarding job tenure, participants were relatively evenly distributed across experience levels with the largest group (29.4%) having served for more than 16 years. In terms of religious affiliation, most participants identified as Muslim (80.2%). Finally, concerning family size, the majority of officers reported having 4–6 family members (65.8%). On average, police personnel reported moderate stress (M = 141.09, SD = 15.59), relatively high mental health (M = 32.67, SD = 4.44) and moderate job satisfaction (M = 69.53, SD = 11.73) (Table 2 ). Table 1 Demographic Characteristics of the Participants (N = 374) Variable Categories n Percentage Age Group 25–30 years 26 7.0 31–36 years 115 30.7 37–42 years 170 45.5 43–48 years 63 16.8 Sex Male 322 86.1 Female 52 13.9 Education SSC 20 5.3 HSC 21 5.6 Honors 45 12.0 Master’s 288 77.0 Income (BDT) Less than 15,000 11 2.9 16,000–25,000 102 27.3 26,000–35,000 132 35.3 Above 36,000 129 34.5 Marital Status Married 340 90.9 Unmarried 34 9.1 Job Tenure Below 5 years 66 17.6 6–10 years 96 25.7 11–15 years 102 27.3 Above 16 years 110 29.4 Religion Hindu 42 11.2 Muslim 300 80.2 Buddhist 32 8.6 Family Members 1–3 persons 105 28.1 4–6 persons 246 65.8 7–9 persons 23 6.1 Note. SSC = Secondary School Certificate; HSC = Higher Secondary Certificate; BDT = Bangladeshi Taka. All percentages calculated based on N = 374. Table 2 Mean, SD and Cronbach’s α among study variables Variables M SD Cronbach’s α 1. Mental Health 32.67 4.44 0.70 2. Job Satisfaction 69.53 11.73 0.86 3. Job Stress 141.09 15.59 0.81 Note. M = Mean; SD = Standard Deviation. [Insert Table 1 about here] [Insert Table 2 about here] Before hypothesis testing, data were screened for missing values, outliers, and violations of statistical assumptions. Missing data were minimal (< 2%) and determined to be missing completely at random; listwise deletion was therefore employed for all analyses (Kline, 2016 ). Preliminary analyses were conducted to ensure no violation of the assumptions of normality, linearity, and homoscedasticity. Normality was assessed through visual inspection of histograms and P-P plots, as well as an examination of skewness and kurtosis values, which were all within acceptable ranges (i.e., absolute values < 2 for skewness and < 7 for kurtosis), indicating that the data approximated a normal distribution. An examination of scatterplots confirmed that the relationships between variables were linear and that the residuals were evenly dispersed. Correlations Pearson correlations (Table 3 ) indicated that job stress was strongly negatively related to mental health (r = –.52, p < .001) and job satisfaction (r = –.69, p < .001), whereas mental health was positively related to job satisfaction (r = .58, p < .001). All three constructs correlated in the expected directions. Table 3 Correlations among study variables Variable 1 2 3 4 5 6 7 8 9 10 11 1. Age — 2. Sex −.21** — 3. Religion .21** .02 — 4. Marital Status −.43** .22** .02 — 5. Education −.02 −.31** −.19** .03 — 6. Tenure .78** −.09 −.08 −.50** .05 — 7. Income .49** −.15** −.02 −.39** .53** .62** — 8. Family Size −.04 .21** −.04 .35** .06 .09 .20** — 9. Mental Health −.12* .35** −.06 .03 .08 .05 .11* −.10 — 10. Job Stress .01 −.16** −.10 .19** .16** −.13* −.14** .28** −.52** — 11. Job Satisfaction .15** .16** .23** −.19** −.08 .27** .22** −.14** .58** −.69** — Note . N = 374 for all variables. *** p < .001. [Insert Table 3 about here] Hierarchical regression Hierarchical multiple regression was conducted to examine predictors of job satisfaction (Table 4 ). Table 4 Hierarchical Multiple Regression Predicting Job Satisfaction Predictor Model 1 β Model 2 β Model 3 β Step 1: Demographics Age −.40*** −.05 −.01 Sex .16** .09* −.03 Religion .32*** .22*** .23*** Marital Status .08 −.01 −.04 Education −.14* .11* .01 Tenure .50*** .27*** .21** Income .31*** −.06 −.04 Family Size −.30*** .02 .05 Step 2: Job Stress Job Stress — −.65*** −.48*** Step 3: Mental Health Mental Health — — .35*** Model Fit R² .28 .55 .62 ΔR² — .28*** .07*** F 17.38*** 49.86*** 59.61*** Note. Standardized beta coefficients (β) are reported. DV = Job Satisfaction. Model 1 includes demographic variables. Model 2 adds Job Stress. Model 3 adds Mental Health; *p < .05; ** p < .01; *** p < .001 In Model 1, demographic variables accounted for 28% of the variance in job satisfaction (R² = .28, F = 17.38, p < .001). Age ( β = –.40, p < .001), sex ( β = .16, p < .01), religion ( β = .32, p < .001), education ( β = –.14, p < .05), tenure ( β = .50, p < .001), income ( β = .31, p < .001), and family size ( β = –.30, p < .001) were significant predictors. In Model 2, adding job stress significantly increased explained variance (ΔR² = .28, p < .001), with the model explaining 55% of the variance (R² = .55, F = 49.86, p < .001). Job stress emerged as a strong negative predictor ( β = –.65, p < .001). In Model 3, mental health was added, resulting in a further significant increase in explained variance (ΔR² = .07, p < .001). The final model explained 62% of the variance in job satisfaction (R² = .62, F = 59.61, p < .001). Mental health was a significant positive predictor ( β = .35, p < .001), and job stress remained significant though reduced in magnitude ( β = –.48, p < .001), this pattern indicative of partial mediation. In the final model, religion and tenure also remained significant predictors. [Insert Table 4 about here] Mediation Analysis The mediating role of mental health in the relationship between job stress and job satisfaction was examined using PROCESS macro (Model 4; Hayes, 2017 ) with 5,000 bootstrap samples. This approach generates bias-corrected confidence intervals for indirect effects. Age, sex, religion, marital status, education, tenure, income, and family size were included as covariates. Job Stress → Mental Health Job stress significantly predicted mental health (B = − 0.14, SE = 0.01, p < .001), indicating that higher levels of job stress were associated with poorer mental health. The mediator model was statistically significant, F (9, 364) = 29.96, p < .001, and explained 43% of the variance in mental health (R² = .43). Direct Effect: Predicting Job Satisfaction In the dependent variable model, mental health significantly predicted job satisfaction (B = 0.92, SE = 0.11, p < .001), controlling for job stress and demographic covariates. Job stress remained a significant negative predictor of job satisfaction (B = − 0.36, SE = 0.03, p < .001). The full model was statistically significant, F (10, 363) = 59.61, p < .001, and explained 62% of the variance in job satisfaction (R² = .62). [Insert Table 5 about here] Table 5 Mediation Model Predicting Mental Health and Job Satisfaction from Job Stress (N = 374) Mediator variable Model Dependent Variable Model Mental Health Job Satisfaction Predictor B SE p B SE p Constant 54.58 1.52 < 0.001 75.15 6.88 < 0.001 Job Stress -0.14 .01 < 0.001 -0.36 .03 < 0.001 Mental Health — — — .92 .11 < 0.001 Age − .53 .43 .209 − .17 .91 .853 Sex 4.44 .59 < 0.001 − .90 1.36 .508 Religion − .34 .46 .458 6.04 .99 < 0.001 Marital Status 1.39 .86 .109 -1.63 1.86 .380 Education 1.50 .34 < 0.001 .20 .74 .790 Tenure .70 .35 .044 2.28 .75 .003 Income − .29 .40 .47 − .52 .87 .553 Family Size − .74 .44 .096 1.04 .95 .275 R²=.43 R²=.62 F (9, 364) = 29.96, p < .001 F (10, 363) = 59.61, p < .001 Note. B = unstandardized coefficient; SE = standard error Total and Indirect Effects The total effect of job stress on job satisfaction was significant (B = − 0.49, SE = 0.03, t = − 15.06, p < .001, 95% CI [–0.549, − 0.423]). When mental health was included in the model, the direct effect remained significant but was reduced in magnitude (B = − 0.36, SE = 0.03, t = − 10.64, p < .001, 95% CI [–0.425, − 0.292]). [Insert Table 6 about here] Table 6 Total, Direct, and Indirect Effects of Job Stress on Job Satisfaction Effect Path B SE t p 95% CI LL 95% CI UL Total Effect JS → JSA −0.49 0.03 −15.06 < .001 −0.549 −0.423 Direct Effect JS → JSA −0.36 0.03 −10.64 < .001 −0.425 −0.292 Indirect Effect JS → MH → JSA −0.13 0.01ᵃ — — −0.153ᵇ −0.102ᵇ Note. B = unstandardized coefficient; SE = standard error. ᵃ Bootstrapped standard error (5,000 samples). ᵇ Bootstrapped 95% confidence interval. The indirect effect is statistically significant because the confidence interval does not include zero. The bootstrapped indirect effect of job stress on job satisfaction through mental health was significant (B = − 0.13, bootstrapped SE = 0.01, 95% CI [–0.153, − 0.102]). Because the confidence interval did not include zero, the indirect effect was statistically significant, supporting Hypothesis 4. These findings indicate that mental health partially mediates the relationship between job stress and job satisfaction. Discussion This study provides evidence that police officers’ mental health mediates the effect of job stress on job satisfaction. Consistent with hypotheses, police officers who reported higher job stress also reported poorer mental health and lower job satisfaction. Conversely, officers with better psychological health reported higher satisfaction. These patterns align with stress–coping theory (Lazarus & Folkman, 1984 ), which posit that excessive demands deplete coping resources. In practical terms, when officers face heavy workload, exposure to danger, or bureaucratic pressures, their well-being suffers and job satisfaction declines. This is in line with prior research showing police face elevated stress and mental health risks (Violanti et al., 2017 ) and that stress typically undermines satisfaction in high-demand jobs (Karasek, 1979 ). Crucially, our mediation analysis confirmed H4, controlling for demographic variables. The significant indirect effect indicates that a substantial portion of stress’s impact on satisfaction is mediated by police employee’s mental health. This finding has theoretical importance because it clarifies the pathway from occupational stress to job attitudes in policing. In other words, employees’ mental health is a key pathway: stress diminishes satisfaction partly because it erodes mental health. This is coherent with the Job Demands–Resources model (Demerouti et al., 2001 ), which posits that job demands (stressors) drain resources (mental health), leading to strain and lower positive outcomes. From a practical standpoint, these results suggest that interventions to improve mental health could help mitigate the harmful effects of stress on job satisfaction. Police departments might invest in mental health resources, such as counselling services, stress management training, peer support to buffer officers from chronic stressors. Violanti et al. ( 2017 ) reviewed evidence that exposure to officer stressors correlates with poorer psychological health; our findings imply that such health impacts, in turn, reduces job satisfaction. Enhancing organizational support may also improve outcomes. Organizations should ensure fair procedures and give officers a sense of control where possible, in order to reinforce psychological resources. Implications Our study underscores the importance of the mental health pathway in police occupational health models. Interventions that strengthen officers’ psychological resilience (e.g., cognitive-behavioral coping skills, mindfulness, adequate rest) are likely to yield dual benefits: better mental health and higher job satisfaction. Training supervisors to recognize signs of distress and to provide support can also attenuate stress–satisfaction links. Additionally, addressing specific stressors such as, reducing excessive overtime, clarifying role expectations could directly reduce both strain and dissatisfaction. Limitations The present study is not without limitations. First, the cross-sectional design precludes causal inference. While theory suggest stress leads to poorer mental health, which leads to lower satisfaction, longitudinal data are needed to confirm the temporal order. Second, the sample was from a single metropolitan police force in Bangladesh using non-probability sampling, so generalizability to other regions or countries is uncertain. Third, measures were self-reported, raising the possibility of common-method bias. However, the use of different validated scales for stress, health and satisfaction partly mitigates this. Fourth, we did not include other potential mediators or moderators, such as burnout, coping strategies, social support hat may influence these relationships. Future research should use multi-wave, multi-source designs across diverse police populations. Conclusion In conclusion, this study provides evidence that mental health is a key mechanism linking job stress to job satisfaction among police employees. Employees facing high stress reported low mental health, which in turn undermines their job satisfaction. This has practical importance: police agencies should not only aim to reduce stressors, but also invest in officers’ mental health through wellness programs and supportive management to maintain wellbeing and performance. By targeting the mental health pathway, interventions may help sustain job satisfaction even in challenging conditions. Future research should replicate and extend these findings, but the present results highlight that supporting officers’ mental health is critical for occupational health in law enforcement. Declarations Ethical Approval This study was conducted in accordance with ethical standards for research involving human participants. Approval was obtained from the appropriate institutional ethics committee. Informed consent was obtained from all participants prior to data collection. Conflict of Interest The authors declare that they have no conflict of interest. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Author Contribution Author Contributions: A.S. conceptualised the study, conducted data analysis, and wrote the manuscript. N.I. assisted with data collection and manuscript editing. M.S.H. assisted in obtaining administrative approval for data collection. Acknowledgement The authors thank the Chattogram Metropolitan Police for their support in facilitating data collection. Data Availability The data that support the findings of this study are available from the corresponding author upon reasonable request. References Anders R, Frapsauce A, Sauvezon C, Gilibert D (2024) Police officer occupational health: a model of organizational constraints, trauma exposure, perceived resources, and agency. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9097729","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":617631382,"identity":"93e1bd7c-c336-4b70-b0a5-441d8b3c395b","order_by":0,"name":"Aklima Sultana","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYDACZobEBx8qJOT4QZyEAhDJA8RseLSwMzw2nHHGxliyAaTFAKSBkBZ+xmfCvG1piQYHQDxitBgcZk5jnMF2OMH4RnbihwcGDIn72c8eYPhQdhiPFra0Bx94DueZ3cjdLAF0WGIPT14C44xz+LTwpBvOkDhcDNSyAaKFIceAmbcNnxb+b9I8BocTN8/I3fwDrIX/jQHzX7xaGNKkeRLSEjdI5G6D2CIBtIURjxbJwwzJhjMO2BhLnHm7zSLBQMK458Ybg4M959JxauE7fyDxwcd/wKhsz91880eFjWx7f47hgx9l1ji1oAMJMHmAaPWjYBSMglEwCrACAEalWhJ7NwPEAAAAAElFTkSuQmCC","orcid":"","institution":"University of Utah","correspondingAuthor":true,"prefix":"","firstName":"Aklima","middleName":"","lastName":"Sultana","suffix":""},{"id":617631383,"identity":"9bbff086-2a03-47a2-b5d4-c86fb5bddd6a","order_by":1,"name":"Nasrin Islam","email":"","orcid":"","institution":"University of Chittagong","correspondingAuthor":false,"prefix":"","firstName":"Nasrin","middleName":"","lastName":"Islam","suffix":""},{"id":617631385,"identity":"efb9445c-e092-4785-b440-fdbbf03213ec","order_by":2,"name":"Md. Shakhawat Hossain","email":"","orcid":"","institution":"University of Chittagong","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Shakhawat","lastName":"Hossain","suffix":""}],"badges":[],"createdAt":"2026-03-11 19:38:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9097729/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9097729/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106260291,"identity":"b5d9f875-5e3b-47c2-9150-af196e52adaa","added_by":"auto","created_at":"2026-04-06 20:47:10","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":13692,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual model of the mediating role of mental health\u003c/p\u003e","description":"","filename":"groupimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9097729/v1/936b0741e72ddf75a60329af.jpeg"},{"id":106403585,"identity":"824f4b9c-0d05-4c59-9b41-e239b4a2076c","added_by":"auto","created_at":"2026-04-08 09:14:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1032455,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9097729/v1/bf2de365-d9e8-4f72-9589-5849f5a57306.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Mediating Role of Mental Health in the Relationship Between Job Stress and Job Satisfaction Among Bangladeshi Police Personnel","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePolice work is widely recognized as one of the most demanding and stressful occupations in the public sector. Officers routinely face life-threatening and traumatic incidents as well as heavy bureaucratic workloads, leading to elevated rates of mental health problems compared to the general population. For example, exposure to human suffering and danger is related to increase risks of PTSD, depression, and suicidal ideation among police officers (Violanti et al., 2017; Cao et al., 2022). These findings underscore that police stressor—both operational (e.g., violence, accidents) and organizational (e.g., long hours, role conflict, inadequate support)—can significantly degrade officers’ well-being.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMental health is a crucial dimension of well-being. The World Health Organization defines it as a state of well-being that enables people to cope with life’s stresses and function effectively (WHO, 2024). Poor mental health (e.g., anxiety, depression) in turn predicts burnout and reduced quality of life (Violanti et al., 2017; Cao et al., 2022). In policing, evidence shows that high job demands and chronic stress are strongly associated with anxiety and depression symptoms (Queirós et al., 2020; Clays et al., 2007). Given this link, an officer under sustained stress is likely to experience impaired psychological health, in line with Lazarus and Folkman’s transactional model of stress and coping (1984), which posits that individuals’ appraisals of stressors affect health outcomes. Notably, the Job Demands–Resources model (Demerouti et al., 2001) also predicts that excessive demands without adequate resources (e.g., support, autonomy) lead to strain and adverse health outcomes.\u003c/p\u003e\n\u003cp\u003eJob satisfaction reflects a worker’s positive appraisal of one’s job, including factors like pay, support, work conditions, and intrinsic meaning (Locke, 1976; Spector, 1997). It is a key predictor of employee well-being and organizational performance. High satisfaction typically relates to higher morale and retention, whereas dissatisfaction correlates with turnover intentions and poor productivity (Judge et al., 2001; Fernández-Salinero et al., 2026). Research in policing indicates that officers’ satisfaction depends heavily on their work environment: supportive supervision, fairness, and recognition enhance satisfaction, whereas role conflict, lack of resources, and work–life imbalance reduce it (Anders et al., 2024). Indeed, Anders et al. (2024) found that organizational justice, decision latitude and social support were among the strongest protective factors for police well-being and job satisfaction. Empirically, higher occupational stress tends to coincide with lower job satisfaction. For example, in a large longitudinal U.S. study, police officers’ stress negatively predicted their later job satisfaction (Mumford et al., 2025). Similarly, healthcare workers and other employees consistently report that high stress and burnout correlate with reduced job satisfaction (Melnyk et al., 2013). Theoretically, high demands and low control undermine satisfaction (Karasek, 1979), whereas resources and coping bolster it (Demerouti et al., 2001).\u003c/p\u003e\n\u003cp\u003ePositive psychological health fosters engagement and satisfaction of work, while poor mental health undermines one’s perception of work. Recent studies confirm a robust positive correlation between mental well-being and job satisfaction (Cao et al., 2022). In particular, they found that individuals with better mental health reported significantly higher job satisfaction, even after accounting for contextual factors.\u003c/p\u003e\n\u003cp\u003eThis suggests that mental health may be a key intervening variable: if stress impairs mental health, and mental health in turn shapes job attitudes, then mental health could mediate the stress–satisfaction relationship. Despite these connections, few studies have formally tested mental health as a mediator between job stress and satisfaction in police samples. One relevant study (Nelson \u0026amp; Smith, 2016) noted that job stress works as a mediator between police officers’ work conditions and their well-being.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHence, understanding whether officers’ mental health explains the link between occupational stress and satisfaction can inform targeted interventions. If mental health is a key pathway, then strengthening psychological resources could buffer the adverse impact of job stress. Guided by the above literature, we posit the following hypotheses:\u003c/p\u003e\n\u003cp\u003eH1: Higher job stress will be associated with poorer mental health among police employees.\u003c/p\u003e\n\u003cp\u003eH2: Better mental health will be positively associated with higher job satisfaction.\u003c/p\u003e\n\u003cp\u003eH3: Higher job stress will be associated with lower job satisfaction.\u003c/p\u003e\n\u003cp\u003eH4: Mental health will mediate the relationship between job stress and job satisfaction,\u003c/p\u003e\n\u003cp\u003esuch that stress leads to poorer mental health, which in turn leads to lower satisfaction.\u003c/p\u003e\n\u003cp\u003eThese hypotheses align with theoretical models of stress (Lazarus \u0026amp; Folkman, 1984) and resources (Demerouti et al., 2001) and with empirical findings that stressors affect psychological health which subsequently influences broader job attitudes.\u003c/p\u003e\n\u003cp\u003e[Insert Figure 1 about here]\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThe study sample comprised of 374 police employees from the Chattogram Metropolitan Police (CMP), which includes 16 Police station of Chittagong district, Bangladesh. All respondents are from different parts of Bangladesh, working at the CMP at the time of data collection. A non-probability sampling method was used for data collection. All respondents are between the ages of 25 and 48. Sample size was determined using Yamane's formula (1967): n\u0026thinsp;=\u0026thinsp;N/(1\u0026thinsp;+\u0026thinsp;N(e)\u0026sup2;), where N\u0026thinsp;=\u0026thinsp;5,785 and e\u0026thinsp;=\u0026thinsp;0.05, yielding the final sample of 374.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eProcedure\u003c/h2\u003e \u003cp\u003eFollowing institutional approval and participants\u0026rsquo; informed consent, questionnaires were distributed to CMP police personnel. The questionnaire packet included demographic items and standardized measures of job stress, mental health, and job satisfaction. Questionnaires were administered individually.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssessments and Measures\u003c/h3\u003e\n\u003cp\u003e \u003cb\u003eBangla version of the Occupational Stress Index (OSI)\u003c/b\u003e. Job stress was measured using the Occupational Stress Index developed by Srivastava and Singh (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1981\u003c/span\u003e). The scale consists of 46 items with 5 response categories (strongly disagree to strongly agree). Of the 46 items, 28 are true-keyed and 18 are false-keyed. The possible range of scores is 46 to 230, with higher scores indicating higher levels of stress. For the present study, the Bangla version adapted by Sorcar and Rahman (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1989\u003c/span\u003e) was used, consists of 46 items with 5 response categories (strongly disagree to strongly agree). In the present study, Cronbach\u0026rsquo;s α was (\u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.81).\u003c/p\u003e \u003cp\u003e \u003cb\u003eBangla version of the General Health Questionnaire (GHQ).\u003c/b\u003e The Bangla version of the General Health Questionnaire (GHQ-12), adapted by Sorcar and Rahman (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1989\u003c/span\u003e), was used to measure the mental health of the participants. GHQ-12, originally developed by Goldberg (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1972\u003c/span\u003e), is designed to detect minor psychiatric disorders in community and primary healthcare settings. The 12-item GHQ was derived from 60 items from the original version. Sorcar and Rahman (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1989\u003c/span\u003e) adapted a new Likert-type scoring system in which true-keyed items (all positively worded items) of their questionnaire, with scores of 0, 1, 2, and 3, were assigned \u0026lsquo;not at all\u0026rsquo;, \u0026lsquo;somewhat\u0026rsquo;, \u0026lsquo;to a considerable extent\u0026rsquo;, and \u0026lsquo;to a great extent\u0026rsquo; respectively. The scoring for the false-keyed items was reversed. The higher the score, the better the mental health. In the present study, Cronbach\u0026rsquo;s α was (\u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.70).\u003c/p\u003e \u003cp\u003e \u003cb\u003eBangla version of Job Satisfaction Scale (JSS).\u003c/b\u003e The Bangla version of the job satisfaction (JSS) scale (Rahman, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) was originally developed by Warr et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1979\u003c/span\u003e). The JSS scale contains 15 statements on different aspects of the job. It is a seven-point Likert-type scale. Response options range from \u0026lsquo;I am not at all satisfied\u0026rsquo; to \u0026lsquo;I am quite satisfied\u0026rsquo;. Scores from 1 to 7 are assigned accordingly. The highest and lowest possible total scores are 105 and 15, respectively, with higher scores indicating greater job satisfaction. In the present study, Cronbach\u0026rsquo;s α was (\u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.86).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 374 police officers from the Chattogram Metropolitan Police (CMP) in Bangladesh participated in this study. Data analysis was conducted using SPSS (Version 26) and the PROCESS macro for SPSS (Hayes, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eParticipant Characteristics\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the demographic characteristics of the sample. Regarding age distribution, the largest group of participants was aged 37\u0026ndash;42 years (45.5%). The sample was predominantly male (86.1%), with female officers accounting for 13.9% of participants. In terms of educational attainment, the majority of police employees held a Master's degree (77%). Monthly income varied across participants, with the largest proportion (35.3%) earning between 26,000 and 35,000 BDT. The majority of participants were married (90.9%). Regarding job tenure, participants were relatively evenly distributed across experience levels with the largest group (29.4%) having served for more than 16 years. In terms of religious affiliation, most participants identified as Muslim (80.2%). Finally, concerning family size, the majority of officers reported having 4\u0026ndash;6 family members (65.8%). On average, police personnel reported moderate stress (M\u0026thinsp;=\u0026thinsp;141.09, SD\u0026thinsp;=\u0026thinsp;15.59), relatively high mental health (M\u0026thinsp;=\u0026thinsp;32.67, SD\u0026thinsp;=\u0026thinsp;4.44) and moderate job satisfaction (M\u0026thinsp;=\u0026thinsp;69.53, SD\u0026thinsp;=\u0026thinsp;11.73) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eDemographic Characteristics of the Participants (N\u0026thinsp;=\u0026thinsp;374)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge Group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026ndash;30 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u0026ndash;36 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37\u0026ndash;42 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43\u0026ndash;48 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHonors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaster\u0026rsquo;s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIncome (BDT)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess than 15,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16,000\u0026ndash;25,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26,000\u0026ndash;35,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbove 36,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eJob Tenure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBelow 5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u0026ndash;10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026ndash;15 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbove 16 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReligion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHindu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMuslim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuddhist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily Members\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;3 persons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u0026ndash;6 persons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u0026ndash;9 persons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote.\u003c/em\u003e SSC\u0026thinsp;=\u0026thinsp;Secondary School Certificate; HSC\u0026thinsp;=\u0026thinsp;Higher Secondary Certificate; BDT\u0026thinsp;=\u0026thinsp;Bangladeshi Taka. All percentages calculated based on N\u0026thinsp;=\u0026thinsp;374.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eMean, SD and Cronbach\u0026rsquo;s α among study variables\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCronbach\u0026rsquo;s α\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. Mental Health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. Job Satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. Job Stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e141.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote. M\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Mean; \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Standard Deviation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e about here]\u003c/p\u003e \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e about here]\u003c/p\u003e \u003cp\u003eBefore hypothesis testing, data were screened for missing values, outliers, and violations of statistical assumptions. Missing data were minimal (\u0026lt;\u0026thinsp;2%) and determined to be missing completely at random; listwise deletion was therefore employed for all analyses (Kline, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Preliminary analyses were conducted to ensure no violation of the assumptions of normality, linearity, and homoscedasticity. Normality was assessed through visual inspection of histograms and P-P plots, as well as an examination of skewness and kurtosis values, which were all within acceptable ranges (i.e., absolute values\u0026thinsp;\u0026lt;\u0026thinsp;2 for skewness and \u0026lt;\u0026thinsp;7 for kurtosis), indicating that the data approximated a normal distribution. An examination of scatterplots confirmed that the relationships between variables were linear and that the residuals were evenly dispersed.\u003c/p\u003e\n\u003ch3\u003eCorrelations\u003c/h3\u003e\n\u003cp\u003ePearson correlations (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) indicated that job stress was strongly negatively related to mental health (r = \u0026ndash;.52, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) and job satisfaction (r = \u0026ndash;.69, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), whereas mental health was positively related to job satisfaction (r = .58, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). All three constructs correlated in the expected directions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eCorrelations among study variables\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. Sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.21**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. Religion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.21**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. Marital Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.43**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.22**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5. Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;.31**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;.19**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6. Tenure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.78**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;.50**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7. Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.49**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;.15**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;.39**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.53**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.62**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8. Family Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.21**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.35**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.20**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9. Mental Health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.12*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.35**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.11*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10. Job Stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;.16**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.19**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.16**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;.13*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;.14**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.28**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;.52**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11. Job Satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.15**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.16**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.23**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;.19**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.27**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.22**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;.14**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.58**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;.69**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003e\u003cem\u003eNote\u003c/em\u003e. N\u0026thinsp;=\u0026thinsp;374 for all variables. *** p \u0026lt; .001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e about here]\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eHierarchical regression\u003c/h2\u003e \u003cp\u003eHierarchical multiple regression was conducted to examine predictors of job satisfaction (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eHierarchical Multiple Regression Predicting Job Satisfaction\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1 \u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2 \u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3 \u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStep 1: Demographics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.40***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.16**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.09*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReligion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.32***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.22***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.23***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.14*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.11*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTenure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.50***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.27***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.21**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.31***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.30***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStep 2: Job Stress\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJob Stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;.65***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;.48***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStep 3: Mental Health\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMental Health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.35***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel Fit\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔR\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.28***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.07***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.38***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.86***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.61***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote.\u003c/em\u003e Standardized beta coefficients (β) are reported. DV\u0026thinsp;=\u0026thinsp;Job Satisfaction. Model 1 includes demographic variables. Model 2 adds Job Stress. Model 3 adds Mental Health; *p \u0026lt; .05; ** p \u0026lt; .01; *** p \u0026lt; .001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn Model 1, demographic variables accounted for 28% of the variance in job satisfaction (R\u0026sup2; = .28, F\u0026thinsp;=\u0026thinsp;17.38, p \u0026lt; .001). Age (\u003cem\u003eβ\u003c/em\u003e = \u0026ndash;.40, p \u0026lt; .001), sex (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.16, p \u0026lt; .01), religion (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.32, p \u0026lt; .001), education (\u003cem\u003eβ\u003c/em\u003e = \u0026ndash;.14, p \u0026lt; .05), tenure (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.50, p \u0026lt; .001), income (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.31, p \u0026lt; .001), and family size (\u003cem\u003eβ\u003c/em\u003e = \u0026ndash;.30, p \u0026lt; .001) were significant predictors.\u003c/p\u003e \u003cp\u003eIn Model 2, adding job stress significantly increased explained variance (ΔR\u0026sup2; = .28, p \u0026lt; .001), with the model explaining 55% of the variance (R\u0026sup2; = .55, F\u0026thinsp;=\u0026thinsp;49.86, p \u0026lt; .001). Job stress emerged as a strong negative predictor (\u003cem\u003eβ\u003c/em\u003e = \u0026ndash;.65, p \u0026lt; .001).\u003c/p\u003e \u003cp\u003eIn Model 3, mental health was added, resulting in a further significant increase in explained variance (ΔR\u0026sup2; = .07, p \u0026lt; .001). The final model explained 62% of the variance in job satisfaction (R\u0026sup2; = .62, F\u0026thinsp;=\u0026thinsp;59.61, p \u0026lt; .001). Mental health was a significant positive predictor (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.35, p \u0026lt; .001), and job stress remained significant though reduced in magnitude (\u003cem\u003eβ\u003c/em\u003e = \u0026ndash;.48, p \u0026lt; .001), this pattern indicative of partial mediation. In the final model, religion and tenure also remained significant predictors.\u003c/p\u003e \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e about here]\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMediation Analysis\u003c/h3\u003e\n\u003cp\u003eThe mediating role of mental health in the relationship between job stress and job satisfaction was examined using PROCESS macro (Model 4; Hayes, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) with 5,000 bootstrap samples. This approach generates bias-corrected confidence intervals for indirect effects. Age, sex, religion, marital status, education, tenure, income, and family size were included as covariates.\u003c/p\u003e\n\u003ch3\u003eJob Stress → Mental Health\u003c/h3\u003e\n\u003cp\u003eJob stress significantly predicted mental health (B = \u0026minus;\u0026thinsp;0.14, SE\u0026thinsp;=\u0026thinsp;0.01, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), indicating that higher levels of job stress were associated with poorer mental health. The mediator model was statistically significant, F (9, 364)\u0026thinsp;=\u0026thinsp;29.96, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, and explained 43% of the variance in mental health (R\u0026sup2; = .43).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDirect Effect: Predicting Job Satisfaction\u003c/h2\u003e \u003cp\u003eIn the dependent variable model, mental health significantly predicted job satisfaction (B\u0026thinsp;=\u0026thinsp;0.92, SE\u0026thinsp;=\u0026thinsp;0.11, p \u0026lt; .001), controlling for job stress and demographic covariates. Job stress remained a significant negative predictor of job satisfaction (B = \u0026minus;\u0026thinsp;0.36, SE\u0026thinsp;=\u0026thinsp;0.03, p \u0026lt; .001). The full model was statistically significant, F (10, 363)\u0026thinsp;=\u0026thinsp;59.61, p \u0026lt; .001, and explained 62% of the variance in job satisfaction (R\u0026sup2; = .62).\u003c/p\u003e \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e about here]\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eMediation Model Predicting Mental Health and Job Satisfaction from Job Stress (N\u0026thinsp;=\u0026thinsp;374)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eMediator variable Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eDependent Variable Model\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eMental Health\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eJob Satisfaction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJob Stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMental Health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.853\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.508\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReligion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.380\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.790\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTenure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.553\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.275\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eR\u0026sup2;=.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eR\u0026sup2;=.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eF (9, 364)\u0026thinsp;=\u0026thinsp;29.96, p \u0026lt; .001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eF (10, 363)\u0026thinsp;=\u0026thinsp;59.61, p \u0026lt; .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cem\u003eNote.\u003c/em\u003e B\u0026thinsp;=\u0026thinsp;unstandardized coefficient; SE\u0026thinsp;=\u0026thinsp;standard error\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eTotal and Indirect Effects\u003c/h2\u003e \u003cp\u003eThe total effect of job stress on job satisfaction was significant (B = \u0026minus;\u0026thinsp;0.49, SE\u0026thinsp;=\u0026thinsp;0.03, t = \u0026minus;\u0026thinsp;15.06, p \u0026lt; .001, 95% CI [\u0026ndash;0.549, \u0026minus;\u0026thinsp;0.423]). When mental health was included in the model, the direct effect remained significant but was reduced in magnitude (B = \u0026minus;\u0026thinsp;0.36, SE\u0026thinsp;=\u0026thinsp;0.03, t = \u0026minus;\u0026thinsp;10.64, p \u0026lt; .001, 95% CI [\u0026ndash;0.425, \u0026minus;\u0026thinsp;0.292]).\u003c/p\u003e \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e about here]\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eTotal, Direct, and Indirect Effects of Job Stress on Job Satisfaction\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePath\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% CI LL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95% CI UL\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJS \u0026rarr; JSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;15.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;0.423\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect Effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJS \u0026rarr; JSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;10.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;0.292\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect Effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJS \u0026rarr; MH \u0026rarr; JSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01ᵃ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.153ᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;0.102ᵇ\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cem\u003eNote.\u003c/em\u003e B\u0026thinsp;=\u0026thinsp;unstandardized coefficient; SE\u0026thinsp;=\u0026thinsp;standard error.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eᵃ Bootstrapped standard error (5,000 samples).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eᵇ Bootstrapped 95% confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eThe indirect effect is statistically significant because the confidence interval does not include zero.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe bootstrapped indirect effect of job stress on job satisfaction through mental health was significant (B = \u0026minus;\u0026thinsp;0.13, bootstrapped SE\u0026thinsp;=\u0026thinsp;0.01, 95% CI [\u0026ndash;0.153, \u0026minus;\u0026thinsp;0.102]). Because the confidence interval did not include zero, the indirect effect was statistically significant, supporting Hypothesis 4. These findings indicate that mental health partially mediates the relationship between job stress and job satisfaction.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides evidence that police officers\u0026rsquo; mental health mediates the effect of job stress on job satisfaction. Consistent with hypotheses, police officers who reported higher job stress also reported poorer mental health and lower job satisfaction. Conversely, officers with better psychological health reported higher satisfaction. These patterns align with stress\u0026ndash;coping theory (Lazarus \u0026amp; Folkman, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1984\u003c/span\u003e), which posit that excessive demands deplete coping resources. In practical terms, when officers face heavy workload, exposure to danger, or bureaucratic pressures, their well-being suffers and job satisfaction declines. This is in line with prior research showing police face elevated stress and mental health risks (Violanti et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and that stress typically undermines satisfaction in high-demand jobs (Karasek, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1979\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCrucially, our mediation analysis confirmed H4, controlling for demographic variables. The significant indirect effect indicates that a substantial portion of stress\u0026rsquo;s impact on satisfaction is mediated by police employee\u0026rsquo;s mental health. This finding has theoretical importance because it clarifies the pathway from occupational stress to job attitudes in policing. In other words, employees\u0026rsquo; mental health is a key pathway: stress diminishes satisfaction partly because it erodes mental health. This is coherent with the Job Demands\u0026ndash;Resources model (Demerouti et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), which posits that job demands (stressors) drain resources (mental health), leading to strain and lower positive outcomes.\u003c/p\u003e \u003cp\u003eFrom a practical standpoint, these results suggest that interventions to improve mental health could help mitigate the harmful effects of stress on job satisfaction. Police departments might invest in mental health resources, such as counselling services, stress management training, peer support to buffer officers from chronic stressors. Violanti et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) reviewed evidence that exposure to officer stressors correlates with poorer psychological health; our findings imply that such health impacts, in turn, reduces job satisfaction. Enhancing organizational support may also improve outcomes. Organizations should ensure fair procedures and give officers a sense of control where possible, in order to reinforce psychological resources.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eImplications\u003c/h2\u003e \u003cp\u003eOur study underscores the importance of the mental health pathway in police occupational health models. Interventions that strengthen officers\u0026rsquo; psychological resilience (e.g., cognitive-behavioral coping skills, mindfulness, adequate rest) are likely to yield dual benefits: better mental health and higher job satisfaction. Training supervisors to recognize signs of distress and to provide support can also attenuate stress\u0026ndash;satisfaction links. Additionally, addressing specific stressors such as, reducing excessive overtime, clarifying role expectations could directly reduce both strain and dissatisfaction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThe present study is not without limitations. First, the cross-sectional design precludes causal inference. While theory suggest stress leads to poorer mental health, which leads to lower satisfaction, longitudinal data are needed to confirm the temporal order. Second, the sample was from a single metropolitan police force in Bangladesh using non-probability sampling, so generalizability to other regions or countries is uncertain. Third, measures were self-reported, raising the possibility of common-method bias. However, the use of different validated scales for stress, health and satisfaction partly mitigates this. Fourth, we did not include other potential mediators or moderators, such as burnout, coping strategies, social support hat may influence these relationships. Future research should use multi-wave, multi-source designs across diverse police populations.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study provides evidence that mental health is a key mechanism linking job stress to job satisfaction among police employees. Employees facing high stress reported low mental health, which in turn undermines their job satisfaction. This has practical importance: police agencies should not only aim to reduce stressors, but also invest in officers\u0026rsquo; mental health through wellness programs and supportive management to maintain wellbeing and performance. By targeting the mental health pathway, interventions may help sustain job satisfaction even in challenging conditions. Future research should replicate and extend these findings, but the present results highlight that supporting officers\u0026rsquo; mental health is critical for occupational health in law enforcement.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthical Approval\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThis study was conducted in accordance with ethical standards for research involving human participants. Approval was obtained from the appropriate institutional ethics committee. Informed consent was obtained from all participants prior to data collection.\u003c/p\u003e\n\u003ch2\u003eConflict of Interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eAuthor Contributions: A.S. conceptualised the study, conducted data analysis, and wrote the manuscript. N.I. assisted with data collection and manuscript editing. M.S.H. assisted in obtaining administrative approval for data collection.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThe authors thank the Chattogram Metropolitan Police for their support in facilitating data collection.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAnders R, Frapsauce A, Sauvezon C, Gilibert D (2024) Police officer occupational health: a model of organizational constraints, trauma exposure, perceived resources, and agency. 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Prentice-Hall\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"job stress, mental health, job satisfaction, police personnel","lastPublishedDoi":"10.21203/rs.3.rs-9097729/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9097729/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePolice personnel face unique occupational challenges that place them at elevated risk for stress-related difficulties. This study examined whether mental health mediates the relationship between job stress and job satisfaction among police personnel in Bangladesh. Data were collected from 374 officers of the Chattogram Metropolitan Police. Correlation analyses indicated that job stress was negatively associated with mental health (r = \u0026ndash;.52, p \u0026lt; .001) and job satisfaction (r = \u0026ndash;.69, p \u0026lt; .001), whereas mental health was positively associated with job satisfaction (r = .58, p \u0026lt; .001). Hierarchical regression analyses controlling for demographic variables showed that job stress was a strong negative predictor of job satisfaction (β = \u0026ndash;.65, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), and mental health was a significant positive predictor (β\u0026thinsp;=\u0026thinsp;.35, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). Mediation analysis using the PROCESS macro with 5,000 bootstrap samples further indicated that job stress significantly predicted poorer mental health (B = \u0026minus;\u0026thinsp;0.14, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), and mental health significantly predicted job satisfaction (B\u0026thinsp;=\u0026thinsp;0.92, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). The indirect effect of job stress on job satisfaction through mental health was significant (B = \u0026minus;\u0026thinsp;0.13, 95% CI [\u0026ndash;0.153, \u0026minus;\u0026thinsp;0.102]), indicating partial mediation. These findings suggest that the relationship between job stress and job satisfaction operates both directly and indirectly through mental health. Interventions targeting stress reduction and mental health support may therefore contribute to improved job satisfaction among police personnel.\u003c/p\u003e","manuscriptTitle":"The Mediating Role of Mental Health in the Relationship Between Job Stress and Job Satisfaction Among Bangladeshi Police Personnel","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-06 20:47:06","doi":"10.21203/rs.3.rs-9097729/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"df1dfb3e-8dec-4e14-9764-c92c0e0056f2","owner":[],"postedDate":"April 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-06T20:47:06+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-06 20:47:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9097729","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9097729","identity":"rs-9097729","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}