Relationships among working hours, job stress, psychological capital, and depressive symptoms among Chinese medical staff: a moderated mediation model

Relationships among working hours, job stress, psychological capital, and depressive symptoms among Chinese medical staff: a moderated mediation model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Relationships among working hours, job stress, psychological capital, and depressive symptoms among Chinese medical staff: a moderated mediation model Jin Wang, Xiao-Man Liu, Shuang Li, Xia Wan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6543738/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 Background Although several studies have revealed an association between long working hours and depressive symptoms, the mechanisms underlying this association are not entirely clear. This study examined the complex interplay among working hours, job stress, psychological capital, and depressive symptoms among Chinese medical staff via a moderated mediation model. Methods Utilizing data from the National Occupational Health Risk Assessment on Long Working Hours program (2021–2023), this cross-sectional study focused on medical staff, including both doctors and nurses, from tertiary Grade A hospitals. Web-based questionnaires were employed to measure variables using the Patient Health Questionnaire-9, Core Occupational Stress Scale, and 24-item Psychological Capital Questionnaire. Data analyses were conducted via SPSS 26.0 and Hayes’ PROCESS macro (Model 4 for mediation; Model 59 for moderated mediation) with 5,000 bootstrap samples to assess direct/indirect effects between working hours, job stress, psychological capital, and depressive symptoms. Results A total of 4,576 participants from 20 tertiary hospitals with valid questionnaires were included. The prevalence of depressive symptoms was 33.9%. Working hours ( r = 0.276, p < 0.001) and job stress ( r = 0.175, p < 0.001) were positively correlated with depressive symptoms, whereas psychological capital was negatively correlated with both job stress ( r = -0.319, p < 0.001) and depressive symptoms ( r = -0.439, P < 0.001). The findings indicate that job stress has an indirect-only mediating effect on the relationship between working hours and depressive symptoms. Additionally, PsyCap moderates the indirect pathway between working hours and depressive symptoms via job stress. Specifically, higher levels of psychological capital weaken the impact of job stress on depressive symptoms. Conclusion Working hours are positively associated with depressive symptoms among Chinese medical staff, with job stress mediating and psychological capital moderating this association. Therefore, addressing medical staff’s mental health requires effective workload management systems and resilience-building interventions aimed at improving psychological resources. Depressive symptoms Working hours Job stress Psychological capital Medical staff Figures Figure 1 Figure 2 Background Mental health, shaped by interconnected social, psychological, behavioral, and biological determinants, profoundly impacts individuals’ daily functioning and occupational performance [1, 2, 3]. Approximately 15% of the global working-age population experiences depression, anxiety and other mental disorders [4], and medical staff, particularly in China, face heightened vulnerability due to heavy workloads, intense pressure, high risk, and frequent patient conflicts [6]. The COVID-19 pandemic further exacerbated these challenges, underscoring mental health as both a public health priority and a care-quality determinant [7, 8]. Studies have consistently shown that medical staff exhibit elevated depression rates [11, 12]. A recently published meta-analysis conducted during the pandemic revealed that the prevalence of depression among healthcare workers was as high as 29%, particularly among frontline workers who experienced cumulative risks from prolonged exposure to high-risk exposure and inadequate psychological support [13]. Many factors contribute to the emergence of depressive symptoms. For medical staff, demanding working hours and overwhelming workloads are significant contributors to adverse physical and mental health outcomes [14, 15]. Lang et al. [16] retrospectively investigated the psychological status of medical workers in China and reported a strong correlation between working hours and depressive symptoms. Through a systematic review and meta-analysis, Virtanen et al. [17] demonstrated a moderate association between long working hours and depressive symptoms in Asia, in contrast with a weaker association in Europe, suggesting that cultural and organizational differences likely mediate this disparity. However, the WHO/ILO Joint Estimates of the Work-related Burden of Disease and Injury conducted a meta-analysis that revealed insufficient evidence to definitively link long working hours to depression incidence [18]. This finding has sparked debate, with critics emphasizing methodological heterogeneity in study designs, measurement tools, and cultural contexts [19]. Others propose that multiple pathways—including psychosocial characteristics, feelings of distress, sleep deprivation, and personality traits—may explain inconsistencies in the literature [20, 21]. Job stress, characterized by excessive workload, time pressure, and an imbalance between high effort and low reward, exerts detrimental effects on mental health. This stressor arises mainly from employees’ interactions with adverse work environments, especially when harmful working conditions combine with long working hours [22]. Empirical evidence consistently indicates that extended work hours significantly exacerbate job stress levels, and chronic exposure to unmitigated job stress constitutes a significant risk factor for the development of mental disorders [23, 24]. For example, a study based on the Korea Working Conditions Survey in 2014 revealed that job stress partially explains the effects of long working hours on depressive symptoms [25]. These findings underscore job stress as a potential mediator in the pathway linking working hours to mental health deterioration. Concurrently, the rise of positive psychology has shifted scholarly attention toward psychological resource-based interventions for work-related mental health issues. Psychological capital (PsyCap), defined as a developable positive psychological state, encompasses four core dimensions: self-efficacy, hope, optimism, and resilience [26]. Research has demonstrated the dual protective function of PsyCap against depressive symptoms through both direct and indirect mechanisms. Meta-analytic data confirmed a robust negative correlation ( r = -0.43) between PsyCap and depression, which was consistent across age groups and publication years [27]. Furthermore, PsyCap has been shown to enhance coping strategies, improve social support networks, and reduce perceived stress, all of which can mitigate the adverse effects of job stress on mental health [28]. While previous research has established a clear link between long working hours and depressive symptoms, the mechanisms underlying this association remain underexplored, especially in the context of Chinese healthcare settings. Furthermore, the potential role of job stress and psychological resources in mitigating these effects has not been thoroughly investigated within this population. Therefore, the current study aims to investigate the prevalence of depressive symptoms in Chinese medical staff and the associations among working hours, job stress, and depressive symptoms, with a specific focus on exploring whether PsyCap moderates these associations. On the basis of previous research, we developed a theoretical hypothesis model (Fig. 1) and propose the following hypotheses: (1) working hours are significantly correlated with depressive symptoms (path H1); (2) job stress mediates the relationship between working hours and depressive symptoms (paths H2 and H3); and (3) PsyCap moderates both the direct (path H5) and indirect effects (paths H4 and H6) of working hours on depressive symptoms. Methods Data and study subjects The current cross-sectional study was carried out on the basis of the Program of Occupational Health Risk Assessment on Long Working Hours (OHRA-LWH), which was conducted by the National Institute of Occupational Health and Poison Control, Chinese for Disease Control and Prevention, and in collaboration with local institutes for the prevention of occupational disease. It is a three-year (from April 2021 to December 2023) national survey designed to assess the health risks associated with exposure to long working hours among key occupational groups in China, including IT engineers, medical staff, schoolteachers, manufacturing workers, couriers and take-out food deliverers. The program employs a stratified, multistage purposive sample strategy to obtain a systematic and representative sample of occupational groups. In the initial stage, 12 provincial cooperative units were recruited nationwide in accordance with the research protocol; in the second stage, each cooperative unit identified and purposively sampled 323 typical enterprises and institutions from various industries to conduct surveys on key occupational groups; in the final stage, stratified cluster sampling was conducted within each enterprise or institution, and individuals of each key occupational group (4 451 IT engineers, 13 641 medical staff, 8 133 school teachers, 13 317 manufacturing workers, 5 624 couriers and take-out food deliverers) were included if they participated in either occupational or routine health examinations during the investigation periods. The program requires participants to be working adults aged 18 years or older who voluntarily enroll. Considering the higher workload and requirements in tertiary public hospitals, the subjects of the current study were limited to medical staff, referring to doctors and nurses, from tertiary Grade A hospitals (n=5 178). Since the retirement ages in China are 60 and 55 for men and women, respectively, and to minimize health-related biases commonly observed after age 60, such as chronic diseases, we set the upper age limit of study participants at 60 years old. We also exclude individuals working fewer than 35 hours per week, as studies indicate that a significant proportion of those working fewer than standard hours do so due to preexisting health issues [29]. The survey was conducted by well-trained researchers via web-based self-administered questionnaires and sought information on a variety of topics, including demographic traits, working conditions, individual lifestyles, psychosocial and mental health status, illnesses and medical conditions. During the investigation, the researcher explained the purpose of the study to the subjects and obtained informed consent. Measures Working Hours In the program, working hours were measured via the following two self-reported questions: (1) “How many hours per day, including overtime and part-time jobs, did you work over the past half year on average?” and (2) “how many days per week did you work over the past half year on average?” The number of average working hours per week (h/week) was calculated by multiplying the results of questions (1) and (2). Working hours were treated as continuous variables in this study. Depressive symptoms Depressive symptoms were evaluated using the Chinese version of the Patient Health Questionnaire–9 (PHQ–9), a well-validated and efficient screening instrument that measures the presence and severity of depressive symptoms [30]. The PHQ–9 is a self-report questionnaire consisting of nine items based on the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) criteria for assessing symptoms of depression experienced over the past two weeks. Each item is rated on a four-point Likert scale ranging from 0 (“not at all”) to 3 (“nearly every day”). The total score of the nine items was categorized into non or minimal (0--4), mild (5--9), moderate (10--14), moderately severe (15--19), and severe (20--27) depressive symptom groups, and increasing scores suggested greater severity of symptoms. In this study, participants with a sum score of 10 or above were defined as exhibiting depressive symptoms [31]. The Cronbach’s α for the PHQ–9 in this study was 0.898. Job stress Job stress was evaluated using the Core Occupational Stress Scale (COSS), which was developed on the basis of key occupational groups in typical industries and has demonstrated strong reliability and validity in China [32]. The COSS comprises 17 items and four subscales: social support, organization and reward, demand and effort, and job control. Each item is rated on a five-point Likert scale ranging from 1 (“totally disagree”) to 5 (“totally agree”). The total score of the COSS is calculated by summing all items after reverse scoring of negatively keyed items. Higher COSS scores indicate greater severity of job stress. In this study, the Cronbach’s α coefficient for the COSS score was 0.837. Psychological capital PsyCap was evaluated using the Chinese version of the 24-item Psychological Capital Questionnaire (PCQ-24) [33]. The PCQ-24 consists of four dimensions: self-efficacy, optimism, resilience, and hope. Each item is rated on a six-point Likert scale ranging from 1 (“strongly disagree”) to 6 (“strongly agree”). The total score of the PCQ was calculated by summing all the items, with higher scores indicating greater levels of PsyCap. The PCQ has demonstrated robust reliability and validity across multiple samples [34]. The Cronbach’s alpha for this scale in the present study was 0.942, indicating excellent internal consistency reliability. Covariates To control for their impact as confounding factors, the following sociodemographic and health-related characteristics were added as covariates: region, gender, age, education level, income, marital status, occupation, years of work experience, night shift work status, smoking and drinking behavior, and frequency of physical activity. The participants were classified into three regions of China—Eastern, Central, and Western—on the basis of variations in economic development levels, resource availability, and natural conditions; four age groups—≤ 25 years, 26--35 years, 36--45 years, and > 45 years; three groups according to educational level—college and below, undergraduate, and graduate and above; and three groups according to average monthly income (Chinese yuan ): less than 5000, 5000--8999, 9000 and more. Night shift work was defined as working at least two hours a day from 10 pm to 5 am and categorized as either yes or no. For individual lifestyle variables, self-reported smoking status was categorized as current smoker, former smoker, or never smoker; alcohol consumption behavior was defined as having consumed any alcoholic beverages in the past month and categorized as either “yes” or “no”; and physical activity (PA) levels were categorized as low, moderate, or high based on exercise frequency: less than 1–3 times per month for low PA, 1–2 times per week for moderate PA, and more than 3 times per week for high PA. Statistical analysis Data analysis was performed via SPSS (version 26.0 for Windows). The participants’ characteristics were summarized by descriptive statistics (i.e., means, standard deviations, and percentages). Differences in depressive symptoms across sociodemographic subgroups were assessed through one-way ANOVA and independent samples t tests. Variables that were statistically significant in the univariate analysis were further analyzed using a multivariate linear regression model. Associations between continuous variables were evaluated using Spearman’s correlation analysis. Statistically significant confounding factors identified through multivariate analysis were treated as covariates in subsequent analyses. Following Zhao et al.’s procedure [35], a structured step-by-step approach was utilized to examine the mediating effect of job stress (M) on the relationship between working hours (X) and depressive symptoms (Y). A moderated mediation analysis [36] was conducted to investigate whether PsyCap (W) moderates the direct and indirect effects of X on Y. The mediation model (Model 4) and the moderated mediation model (Model 59) were implemented via the PROCESS macro plug-in for SPSS (version 3.5) [37]. The bootstrapping method (5000 bootstrapping samples) with bias-corrected 95% confidence intervals (CIs) was used to evaluate effect significance. Categorical variables were dummy-coded, and continuous variables were standardized to minimize scale heterogeneity. To further illustrate the moderating effect, simple slope analyses were conducted in accordance with Aiken and West’s procedures [38], and the conditional effects were evaluated at one standard deviation ( SD ) above the mean, at the mean, and at one SD below the mean for PsyCap, which served as the moderator variable of interest. Statistical significance was determined at a P value < 0.05 (two-tailed). Results Participants’ characteristics and depressive symptoms After excluding participants who did not meet the inclusion criteria (n = 446) and those with invalid data (n=156), 4 576 valid questionnaires were obtained from 20 tertiary hospitals covering the eastern, middle and western regions of China. Table 1 shows the participants’ sociodemographic and health-related characteristics. Overall, the participants in this study had a mean age of 35.23 years ( SD =8.71 years); most of them were females, accounting for 78.2% of the participants. The majority of the participants were undergraduates (57.5%) and had a monthly income of more than 9000 yuan . According to the PHQ–9 scores, 1552 (33.9%) of the participants were classified as having depressive symptoms; among them, the percentages of mild, moderate, moderately severe, and severe depressive symptoms were 48.8%, 21.9%, 9.4%, and 2.6%, respectively. The results of the independent sample t test and one-way ANOVA illustrate the significant differences in depressive symptoms among medical staff across various sociodemographic and health-related characteristics, including region, sex, age, education level, occupation, working years, night shift work, smoking behavior, risky drinking behavior, and frequency of physical activity (Table 1). Table 1 Participants’ characteristics and univariate analysis of variables related to depressive symptoms among Chinese medical staff (n=4 576) Variables N (%) PHQ–9 ( M ± SD ) F / t P value Region Eastern 3134 (68.5) 8.74 ± 4.79 6.792 0.001 Central 753 (16.5) 8.66 ± 5.01 Western 689 (15.0) 7.99 ± 4.67 Gender male 1134 (24.8) 9.07 ± 5.14 3.491 45 633 (13.8) 8.18 ± 4.43 Education level College and below 1049 (22.9) 8.37 ± 4.95 8.727 < 0.001 Undergraduate 2632 (57.5) 8.51 ± 4.70 Graduate and beyond 895 (19.6) 9.20 ± 4.86 Monthly income (CNY) < 5000 776 (17.0) 8.46 ± 4.86 0.593 0.553 5000-8999 1812 (39.6) 8.68 ± 4.92 ≥ 9000 1988 (43.4) 8.61 ± 4.67 Marital status Married 1304 (28.5) 8.64 ± 4.83 0.373 0.688 Unmarried 3146 (68.7) 8.59 ± 4.79 Others 128 (2.8) 8.96 ± 4.83 Occupation Physicians 2006 (43.8) 8.86 ± 4.82 2.994 0.003 Nurses 2570 (56.2) 8.43 ± 4.78 Working years ≤ 5 1235 (27.0) 8.36 ± 4.81 3.843 0.004 6-10 976 (21.4) 8.81 ± 4.82 11-15 839 (18.3) 8.62 ± 4.80 16-20 639 (14.0) 9.16 ± 5.05 > 20 884 (19.3) 8.36 ± 4.55 Night shifts No 1263 (27.6) 7.72 ± 4.49 - 8.100 < 0.001 Yes 3313 (72.4) 8.96 ± 4.87 Smoking status Current 266 (5.8) 9.95 ± 4.95 19.624 < 0.001 Former 182 (4.0) 9.96 ± 5.51 Never 4128 (90.2) 8.47 ± 4.74 Drinking habits Yes 2253 (49.2) 9.18 ± 4.77 8.006 < 0.001 No 2323 (50.8) 8.06 ± 4.77 PA frequency Low 1853 (40.6) 9.23 ± 4.88 31.439 < 0.001 Moderate 1905 (41.6) 8.40 ± 4.70 High 819 (17.8) 7.72 ± 4.68 Note: M , Mean; SD , Standard deviation; CNY, Chinese Yuan ; PA, Physical activity. Correlation analysis The average working hours of the participants were 50.36 ± 11.55 h/week, ranging from 35--112 h/week. The participants scored 8.61 ± 4.80 on depressive symptoms, 44.35 ± 8.97 on job stress, and 103.69 ± 18.80 on psychological capital. Table 2 provides the M , SD , and Spearman’s correlations between working hours, job stress, psychological capital and depressive symptoms. These results indicate that working hours were positively correlated with job stress ( r = 0.276, p < 0.001) and depressive symptoms ( r = 0.175, p < 0.001). Moreover, job stress was positively correlated with depressive symptoms ( r = 0.527, p < 0.001). In contrast, negative correlations between PsyCap and job stress ( r = -0.319, p < 0.001) and depressive symptoms ( r = -0.439, P < 0.001) were found. Table 2 Correlations between working hours, job stress, psychological capital and depressive symptoms (n=4 576) Variables Mean ± SD 1 2 3 4 Working hours 50.36 ± 11.55 1 job stress 44.35 ± 8.97 0.276 ** 1 psychological capital 103.69 ± 18.80 -0.060 ** -0.319 ** 1 depressive symptoms 8.61 ± 4.80 0.175 ** 0.527 ** - 0.439 ** 1 Note: * p < 0.05 ** p < 0.01 Multivariate linear regression analysis The results of the multiple linear regression analysis for depressive symptoms are presented in Table 3. Depressive symptoms were significantly associated with the following factors: residing in the western region ( B = - 0.608, p = 0.003), having graduated and beyond the education level ( B = 0.648, p = 0.017), working night shifts ( B = 1.270, p < 0.001), never smoking ( B = - 1.144, p < 0.001), engaging in risky drinking behavior ( B = - 0.097, p < 0.001), and having moderate/high PA frequency ( B = - 0.963/- 1.170, both p < 0.001). Table 3 Multiple linear regression analysis of variables related to depressive symptoms (n=4 576) Variables B t P value tolerance VIF Regions (ref. Eastern) Central 0.099 0.504 0.614 0.911 1.097 Western - 0.608 - 2.986 0.003 0.903 1.107 Gender (ref. Male) 0.133 0.639 0.523 0.597 1.674 Undergraduate 0.095 0.518 0.604 0.581 1.723 Graduate and beyond 0.648 2.387 0.017 0.413 2.419 Position (ref. Physicians) 0.003 0.016 0.987 0.509 1.966 Working years 0.022 2.698 0.614 0.911 1.097 Night shift (ref. None) 1.270 7.630 < 0.001 0.864 1.157 Smoking (ref. Current) Former 0.300 0.662 0.508 0.611 1.636 Never - 1.144 - 3.500 < 0.001 0.507 1.971 Drinking (ref. Yes) -0.900 - 6.067 < 0.001 0.870 1.150 PA frequency (ref. Low) Moderate - 0.963 - 6.240 < 0.001 0.827 1.209 High - 1.170 - 8.499 < 0.001 0.805 1.243 F 19.277 ** Adjusted R 2 0.049 Note: B = unstandardized coefficient. ** p < 0.01 Mediation analysis Table 4 presents the results of the mediation analysis examining the relationship between working hours and depressive symptoms via job stress. After adjusting for covariates, the analysis confirmed a significant association between working hours and job stress (path H2: B = 0.212, 95% CI: 0.190, 0.234), as well as a positive effect of job stress on depressive symptoms (path H3: B = 0.288, 95% CI: 0.275, 0.302). Analyses with bootstrap estimates (based on 5 000 bootstrap samples) indicated that the indirect effect through job stress was significant, with a point estimate of 0.061 and a bootstrapping 95% CI excluding zero (0.053, 0.069). However, the total effect of working hours became nonsignificant when job stress was included in the model (path H1: B = 0.001, 95% CI: -0.011, 0.011). This pattern of results supports an indirect-only mediation model as described by Zhao et al. [35]. Table 4 Mediation analysis between working hours, job stress and depressive symptoms (n=4 576) Variables B SE t LLCI ULCI Outcome: Job stress Working hours (path H2) 0.212 0.011 18.834 ** 0.190 0.234 R 2 0.116 F 75.010 Outcome: depressive symptoms Working hours (path H1) 0.001 0.005 0.412 - 0.011 0.011 Job stress (path H3) 0.288 0.007 41.659 ** 0.275 0.302 R 2 0.326 F 245.618 Note: ** p < 0.01 Covariates include region, education level, night shifts, smoking status, drinking habits, and physical activity frequency. Moderated mediation analysis Table 5 shows the findings of the moderated mediation analysis treating job stress as the mediator and PsyCap as a moderator in the association between working hours and depressive symptoms. The analysis revealed a significant moderating effect of PsyCap on the path from job stress to depressive symptoms (H6: B = - 0.002, 95% CI: - 0.003, - 0.001). In contrast, PsyCap did not significantly moderate either the direct effect of working hours on depressive symptoms (H5: B = - 0.001, 95% CI: - 0.001, 0.001) or the indirect effect of working hours on job stress (H4: B = 0.001, 95% CI: - 0.001, 0.002). To visualize the interaction between job stress and PsyCap in relation to depressive symptoms, follow-up simple slope analysis (Figure 1) revealed that the line for higher PsyCap exhibited a steeper slope. These findings suggest that a higher level of PsyCap mitigates the impact of job stress on depressive symptoms. Table 5 Moderated mediation analysis between working hours, job stress, psychological capital and depressive symptoms (n = 4 576) Variables B SE t LLCI ULCI Outcome: Job stress Working hours 0.192 0.010 18.680 ** 0.172 0.213 Psychological capital - 0.192 0.006 - 30.615 - 0.205 - 0.180 Working hours*psychological capital (path H4) 0.001 0.001 1.109 - 0.001 0.002 R 2 0.267 F 116.117 Outcome: depressive symptoms Working hours 0.006 0.005 1.081 - 0.005 0.016 Job stress 0.218 0.007 30.477 ** 0.204 0.232 Psychological capital - 0.082 0.003 - 24.790 ** - 0.089 - 0.076 Working hours*psychological capital (path H5) - 0.001 0.001 - 0.716 - 0.001 0.001 Job stress*psychological capital (path H6) - 0.002 0.001 - 4.430 - 0.003 - 0.001 R 2 0.413 F 267.739 Note: ** P < 0.01 Covariates include region, education level, night shifts, smoking status, drinking habits, and physical activity frequency. Discussion This study aimed to investigate the prevalence of depressive symptoms and analyze the impact of job stress and PsyCap on the relationship between working hours and depressive symptoms using a moderated mediation model. To our knowledge, the study provides the first empirical evidence on how job stress mediates the association between working hours and depressive symptoms, and PsyCap moderates this pathway among Chinese medical staff. The findings demonstrated that working hours were positively correlated with depressive symptoms. Job stress was identified as an indirect-only mediator, whereas PsyCap was found to moderate the indirect effect between working hours and depressive symptoms. Specifically, PsyCap buffered the mediating effect of working hours on depressive symptoms via job stress. The study data revealed notable variations in depressive symptom prevalence among medical staff. Our findings demonstrate a 33.9% prevalence rate among physicians and nurses—a figure surpassing both the general Chinese population (15.81%) [ 39 ] and a nationwide hospital staff cohort (21.4%) [ 40 ], despite the use of identical PHQ–9 criteria. This discrepancy may be attributable to our exclusive focus on clinical practitioners bearing heavier clinical and research responsibilities, excluding other nonclinical or administrative staff. Regional studies have revealed substantial geographic heterogeneity in depression rates among Chinese medical staff. For example, a Heilongjiang Province study utilizing the Self–Rating Depression Scale (SDS) [ 39 ] identified depressive symptoms in 41.6% of physicians, whereas Yin et al. (2023) [ 40 ] reported a markedly higher prevalence of 49.8% among frontline clinicians using the PHQ–9 with a lower diagnostic threshold (cutoff ≥ 5). These discrepancies may be attributed to methodological variations in assessment tools (PHQ–9 vs. SDS) and screening thresholds (≥ 5 vs. ≥10). Our study confirmed that working hours were positively correlated with depressive symptoms, which is consistent with previous findings across various occupations [ 43 , 44 , 45 ]. Notably, this study reveals an indirect-only mediating effect of job stress on the relationship between working hours and depressive symptoms among Chinese medical staff. This suggests that working hours may not directly lead to depressive symptoms but rather act through the exacerbation of job stress. The job strain model posits that high psychological demands paired with high job control promote learning and a sense of mastery, thereby promoting beneficial health; conversely, high psychological demands coupled with low job control hinder learning, induce strain, and ultimately pose risks to health [ 46 ]. Similarly, it is conceivable that the impact of long working hours on depression risk may be more pronounced when accompanied by low job control and other adverse psychosocial factors, such as poor leadership quality or significant role conflict, than in situations without these additional factors [ 47 , 48 ]. While our study identifies indirect-only mediation by job stress, the literature presents mixed evidence. Hong et al. demonstrated that working hours had direct and indirect effects on the depression of couriers in China through the mediating role of job stress [ 49 ]. A Korean cohort study also revealed that job stress mediated only 20–40% of the association between working hours and depression among employees [ 25 ]. These discrepancies may arise from the fact that Chinese medical staff face dual pressures from extreme workloads and strained doctor‒patient relationships, potentially amplifying the mediating role of stress. A recent survey of 992 healthcare workers in China indicated that job burnout plays a mediating role in the relationship between long working hours and depressive symptoms [ 42 ]. The conceptual hierarchy suggests that job stress may act as a proximal mediator, whereas burnout represents a downstream consequence of chronic, unmitigated stress [ 50 , 51 ]. Our cross-sectional design likely captured the earlier stages of this cascade, where job stress fully accounts for the link between working hours and depression. In contrast, Yin et al. [ 42 ] identified burnout as a mediator that may reflect the sampling of participants with prolonged exposure to unrelieved stressors, allowing burnout to develop as a secondary pathway. The moderated mediation analysis provided nuanced insights into the role of PsyCap in occupational mental health. Specifically, our findings demonstrate a conditional indirect effect where PsyCap moderates the relationship between working hours and depressive symptoms through job stress (path H6). However, PsyCap did not have a moderating effect on either the direct association between working hours and depressive symptoms (path H5) or the initial indirect pathway from working hours to job stress (path H4). This pattern suggests that PsyCap functions primarily as a resilience reservoir during the critical transition from job stress to mental health deterioration rather than as a frontline buffer against work-hour exposure or initial stress generation. This pathway-specific moderating effect is supported by the theory of Conservation of Resources (COR) in occupational contexts, where PsyCap, as a personal resource caravan, likely becomes activated specifically during resource depletion phases following prolonged stress exposure [ 52 ]. While excessive working hours may gradually drain psychological resources, PsyCap appears to mitigate subsequent depressive symptom development once stress accumulation surpasses individual coping thresholds [ 53 , 54 ]. This explains why PsyCap's buffering effect only emerges at the job stress‒depression transition, where sustained resource depletion requires active replenishment through hope, efficacy, and optimism. The observed pattern is comparable with Ji et al.’s [ 55 ] findings on the interaction effect of long working hours and effort-reward imbalance (ERI) on depressive symptoms among Chinese village doctors. Our results suggest that PsyCap plays a critical role in intervention during the later stages of this pathway. This selective moderating effect aligns with and distinctively extends previous findings. Earlier research identified social support systems (both familial and organizational) as effective buffers that weaken the burnout‒depression link through similar pathway moderation [ 42 ]. The current results complement this evidence by demonstrating that internal psychological resources (PsyCap) and external support systems may operate through parallel protective mechanisms at different stages of stress. It can also be inferred from the slope that higher PsyCap and lower job stress attenuate depressive symptoms. Our study highlights several potential practical implications. First, considering that long working hours influence depressive symptoms primarily through increased job stress levels, institutions should prioritize the implementation of workload management systems, such as rational shift scheduling and mandatory rest periods, along with stress-reduction programs specifically designed for medical staff. Second, the moderating role of PsyCap in the transition from job stress to depression indicates that resilience-building interventions targeting psychological resources such as efficacy, optimism, and hope can disrupt the pathway from chronic stress to mental health decline. Training programs incorporating cognitive‒behavioral techniques, mindfulness, or PsyCap development modules can equip medical staff with effective tools to mitigate the impact of stress. This study has several limitations. First, the cross-sectional design limits causal conclusions about the temporal relationships between variables. Longitudinal studies are needed to clarify whether long working hours increase job stress and subsequently worsen depressive symptoms. Second, self-reported data may introduce biases such as recall inaccuracies or socially desirable responses, particularly for sensitive outcomes such as depressive symptoms. Future research should use multiple assessment methods, including objective work-hour tracking and clinical evaluations of depression. Third, while PsyCap moderates the pathway from job stress to depressive symptoms, its limited effect on direct working hour impacts and initial stress generation suggests that unmeasured factors—such as social support and leadership quality—may also influence these stages. Future research models should incorporate additional mediators, such as burnout, and moderators, such as organizational resources, to comprehensively capture the full mechanism involved. Finally, the study did not account for potential confounding factors such as sleep quality, family responsibilities, and pandemic-related stressors, which may interact with working hours and mental health outcomes. Addressing these gaps could refine interventions targeting the complex interplay between occupational demands and psychological well-being in healthcare settings. Conclusions Owing to the significant imbalance in healthcare service demands and a shortage of medical workers in China, Chinese medical staff now face unprecedentedly heavy workloads that directly contribute to the development of mental health disorders. The psychological well-being of medical staff is linked to healthcare service quality and patient safety, making it a pressing public health priority. The present study reveals the increased prevalence of depressive symptoms in this population and confirms that extended working hours are positively associated with depressive symptoms. In particular, job stress and PsyCap act as mediators and moderators, respectively, influencing this association. Effective interventions for depressive symptoms in healthcare settings should integrate organizational reforms, such as reducing excessive working hours and alleviating job stress, with resilience-building programs that increase PsyCap and empower medical staff to better manage work-related stressors. These evidence-based findings offer actionable insights for policymakers and healthcare administrators to protect the mental health of medical workers, thereby enhancing the sustainability of China’s healthcare system. Declarations Ethics approval and consent to participate This study was conducted in strict compliance with the World Medical Association Declaration of Helsinki and the National Guidelines on Ethical Review Measures for Life Sciences and Medical Research Involving Human Subjects. Ethical approval was granted by the ethics committee of the National Institute of Occupational Health and Poison Control, Chinese Center for Disease Control and Prevention (Approval number: NIOHP202108). All the subjects provided e-signed informed consent, which could not be correlated with their questionnaires, and all the data were anonymous and untraceable. Clinical Trial Number Not applicable. Consent for publication Not applicable. No individual-level data are presented within this publication. Availability of data and materials The data that support the findings of this study are available from the Institute of Occupational Health and Poison Control, China CDC, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Institute of Occupational Health and Poison Control, China CDC. Competing interests The authors declare that they have no competing interests. Funding This research was supported by the the National Key Research and Development Program of China, Sub-project “Research on Key Technologies and Intervention Strategies for the Prevention and Control of Work-related Diseases and Occupational Injuries” (Grant No. 2022YFC2503205). Authors’ contributions JW conceptualized the study, conducted the analysis, and wrote the first draft of the manuscript. XML managed data collection and was responsible for quality control. SL obtained funding and supervised the study. XW oversaw the analysis and contributed to interpreting the data. All authors read and approved the final manuscript. Acknowledgments We would like to express our gratitude to all the participants in the project and acknowledge the assistance provided by the staff from the Guangdong Provincial Hospital for Occupational Disease Prevention and Treatment, the Shanghai Municipal Center for Disease Control and Prevention, the Second People’s Hospital of Heilongjiang Province, and the Third People’s Hospital of the Xinjiang Uygur Autonomous Region in participant recruitment. References Taubman DS, Parikh SV. Understanding and Addressing Mental Health Disorders: a Workplace Imperative. Curr Psychiatry Rep. 2023;25(10):455-463. https://doi.org/10.1007/s11920-023-01443-7 Strudwick J, Gayed A, Deady M, Haffar S, Mobbs S, Malik A, Akhtar A, Braund T, Bryant RA, Harvey SB. Workplace mental health screening: a systematic review and meta-analysis. Occup Environ Med. 2023;80(8):469-484. https://doi.org/10.1136/oemed-2022-108608 Afaf Khalid, Jawad Syed. Mental health and well-being at work: A systematic review of literature and directions for future research. Human Resource Management Review. 2024;34(1). https://doi.org/10.1016/j.hrmr.2023.100998 WHO guidelines on mental health at work. Geneva: World Health Organization; 2022. Kelloway EK, Dimoff JK, Gilbert S. Mental health in the workplace. Annu Rev Organ Psychol Organ Behav. 2023;10(1):363–387. https://doi.org/10.1146/annurev-orgpsych-120920-050527 He Y, Holroyd E, Koziol-McLain J. Understanding workplace violence against medical staff in China: a retrospective review of publicly available reports. BMC Health Serv Res. 2023;23(1):660-672. https://doi.org/10.1186/s12913-023-09577-3 Zhu H, Yang X, Xie S, Zhou J. Prevalence of burnout and mental health problems among medical staff during the COVID-19 pandemic: a systematic review and meta-analysis. BMJ Open. 2023;20;13(7):e061945. https://doi.org/10.1136/bmjopen-2022-061945 Liu Q, Luo D, Haase JE, Guo Q, Wang XQ, Liu S, Xia L, Liu Z, Yang J, Yang BX. The experiences of health-care providers during the COVID-19 crisis in China: a qualitative study. Lancet Glob Health. 2020;8(6):e790-e798. https://doi.org/10.1016/S2214-109X(20)30204-7 Institute of Health Metrics and Evaluation. Global Health Data Exchange (GHDx). https://vizhub.healthdata.org/gbd-results/(Accessed 4 November 2024). Zhou J, Zhou J, Feng L, Feng Y, Xiao L, Chen X, Yang J, Wang G. The associations between depressive symptoms, functional impairment, and quality of life, in patients with major depression: undirected and Bayesian network analyses. Psychol Med. 2023 ;53(14):6446-6458. https://doi.org/10.1017/S0033291722003385 Pappa S, Ntella V, Giannakas T, Giannakoulis VG, Papoutsi E, Katsaounou P. Prevalence of depression, anxiety, and insomnia among healthcare workers during the COVID-19 pandemic: A systematic review and meta-analysis. Brain Behav Immun. 2020;88:901-907. https://doi.org/10.1016/j.bbi Dragioti E, Tsartsalis D, Mentis M, Mantzoukas S, Gouva M. Impact of the COVID-19 pandemic on the mental health of hospital staff: An umbrella review of 44 meta-analyses. Int J Nurs Stud. 2022;131:104272. https://doi.org/10.1016/j.ijnurstu.2022.104272 Hu N, Deng H, Yang H, Wang C, Cui Y, Chen J, Wang Y, He S, Chai J, Liu F, Zhang P, Xiao X, Li Y. The pooled prevalence of the mental problems of Chinese medical staff during the COVID-19 outbreak: A meta-analysis. J Affect Disord. 2022;303(15):323-330. https://doi.org/10.1016/j.jad.2022.02.045 Virtanen M, Ferrie JE, Singh-Manoux A, Shipley MJ, Stansfeld SA, Marmot MG, Ahola K, Vahtera J, Kivimäki M. Long working hours and symptoms of anxiety and depression: a 5-year follow-up of the Whitehall II study. Psychol Med. 2011 Dec;41(12):2485-94. https://doi.org/10.1017/S0033291711000171 Wong K, Chan AHS, Ngan SC. The Effect of Long Working Hours and Overtime on Occupational Health: A Meta-Analysis of Evidence from 1998 to 2018. Int J Environ Res Public Health. 2019;16(12):2102. https://doi.org/10.3390/ijerph16122102 Lang Q, Liu X, He Y, Lv Q, Xu S. Association between Working Hours and Anxiety/Depression of Medical Staff during Large-Scale Epidemic Outbreak of COVID-19: A Cross-Sectional Study. Psychiatry Investig. 2020;17(12):1167-1174. https://doi.org/10.30773/pi.2020.0229 Virtanen M, Jokela M, Madsen IE, Magnusson Hanson LL, Lallukka T, Nyberg ST, et al. Long working hours and depressive symptoms: systematic review and meta-analysis of published studies and unpublished individual participant data. Scand J Work Environ Health. 2018;44(3):239-250. https://doi.org/10.5271/sjweh.3712 Rugulies R, Sørensen K, Di Tecco C, Bonafede M, Rondinone BM, Ahn S, et al. The effect of exposure to long working hours on depression: A systematic review and meta-analysis from the WHO/ILO Joint Estimates of the Work-related Burden of Disease and Injury. Environ Int. 2021;155:106629. https://doi.org/10.1016/j.envint.2021.106629 Kivimäki M, Virtanen M, Nyberg ST, Batty GD. The WHO/ILO report on long working hours and ischemic heart disease - Conclusions are not supported by the evidence. Environ Int. 2020;144:106048. https://doi.org/10.1016/j.envint.2020.106048 Choi E, Choi KW, Jeong HG, Lee MS, Ko YH, Han C, Ham BJ, Chang J, Han KM. Long working hours and depressive symptoms: moderation by gender, income, and job status. J Affect Disord. 2021;286:99-107. https://doi.org/10.1016/j.jad.2021.03.001 Liu X, Wang C, Wang J, Ji Y, Li S. Effect of long working hours and insomnia on depressive symptoms among employees of Chinese internet companies. BMC Public Health. 2021;21(1):1408. https://doi.org/10.1186/s12889-021-11454-9 Lim JY, Kim GM, Kim EJ. Factors Associated with Job Stress among Hospital Nurses: A Meta-Correlation Analysis. Int J Environ Res Public Health. 2022 May 10;19(10):5792. https://doi.org/10.3390/ijerph19105792 Baek S-Uk ,Yoon J-H. Effect of long working hours on psychological distress among young workers in different types of occupation. Prev Med. 2024;179. https://doi.org/10.1016/j.ypmed.2023.107829 van der Molen HF, Nieuwenhuijsen K, Frings-Dresen MHW, de Groene G. Work-related psychosocial risk factors for stress-related mental disorders: an updated systematic review and meta-analysis. BMJ Open. 2020;10(7):e034849. https://doi.org/10.1136/bmjopen-2019-034849 Yoon Y, Ryu J, Kim H, Kang CW, Jung-Choi K. Working hours and depressive symptoms: the role of job stress factors. Ann Occup Environ Med. 2018;;30:46. https://doi.org/10.1186/s40557-018-0257-5 Claude-Hélène M, Vanderheiden E. Contemporary Positive Psychology Perspectives and Future Directions. International Review of Psychiatry. 2020;32(7–8):537–41. https://doi.org/10.1080/09540261.2020.1813091 Song R, Song L. The relation between psychological capital and depression: a meta-analysis. Curr Psychol. 2024;43:18056–18064. https://doi.org/10.1007/s12144-024-05626-0 Wang MF, Shao P, Wu C, Zhang LY, Zhang LF, Liang J, Du J. The relationship between occupational stressors and insomnia in hospital nurses: The mediating role of psychological capital. Front Psychol. 2023;13:1070809. https://doi.org/10.3389/fpsyg.2022.1070809 Kivimäki M, Jokela M, Nyberg ST, Singh-Manoux A, Fransson EI, Alfredsson L, et al. Long working hours and risk of coronary heart disease and stroke: a systematic review and meta-analysis of published and unpublished data for 603,838 individuals. Lancet. 2015; 31;386(10005):1739-46. https://doi.org/10.1016/S0140-6736(15)60295-1 Wang W, Bian Q, Zhao Y, Li X, Wang W, Du J, Zhang G, Zhou Q, Zhao M. Reliability and validity of the Chinese version of the Patient Health Questionnaire (PHQ–9) in the general population. Gen Hosp Psychiatry. 2014;36(5):539-44. https://doi.org/10.1016/j.genhosppsych.2014.05.021 Manea L, Gilbody S, McMillan D. Optimal cutoff score for diagnosing depression with the Patient Health Questionnaire (PHQ–9): a meta-analysis. CMAJ. 2012;184(3):E191-6. https://doi.org/10.1503/cmaj.110829 Wang J, Zhang QY, Chen HQ, Sun DY, Wang C, Liu XM, Sun YY, Li S, Yu SF. [Development of the Core Occupational Stress Scale for occupational populations in China]. Zhonghua Yu Fang Yi Xue Za Zhi. 2020;6;54(11):1184-1189. Chinese. https://doi.org/10.3760/cma.j.cn112150-20200319-00383 Luthans F, Youssef CM, Avolio BJ. Psychological Capital: Developing the Human Competitive Edge. Psychol Cap Dev Hum Compet Edge. 2007;1:1-256. Wang J, Yuan Z, He H, Jin M, Zeng L, Teng M, Ren Q. Development and psychometric testing of the psychological capital questionnaire for nurses. BMC Nurs. 2024;23:946. https://doi.org/10.1186/s12912-024-02633-1 Zhao XS, Lynch J, Chen QM. Reconsidering Baron and Kenny: Myths and Truths about Mediation Analysis. J Consumer Res. 2010;37(2):197-206. https://doi.org/10.1086/651257 Edwards JR, Lambert LS. Methods for integrating moderation and mediation: A general analytical framework using moderated path analysis. Psychol. Methods. 2007;12:1-22. Hayes AF. Partial, conditional, and moderated moderated mediation: quantification, inference, and interpretation. Commun Monogr. 2018;85(1):4–40. https://doi.org/10.1080/03637751.2017.1352100 Aiken LS, West SG, Reno RR. Multiple regression: Testing and interpreting interactions. SAGE Publications. 1991. Liu J, Wu J, Wang J, Chen S, Yin X, Gong Y. Prevalence and associated factors for depressive symptoms among the general population from 31 provinces in China: The utility of social determinants of health theory. J Affect Disord. 2024;347:269-277. https://doi.org/10.1016/j.jad.2023.10.134 Mei Q, Li W, Feng H, Zhang J, Li J, Yin J, et al. Chinese hospital staff in anxiety and depression: Not only comfort patients but also should be comforted - A nationwide cross-sectional study. J Affect Disord. 2024;360:126-136. https://doi.org/10.1016/j.jad.2024.05.143 Sun L, Zhang Y, He J, Qiao K, Wang C, Zhao S, et al. Relationship between psychological capital and depression in Chinese physicians: The mediating role of organizational commitment and coping style. Front Psychol. 2022;13:904447. https://doi.org/10.3389/fpsyg.2022.904447 Yin C, Ji J, Cao X, Jin H, Ma Q, Gao Y. Impact of long working hours on depressive symptoms among COVID-19 frontline medical staff: The mediation of job burnout and the moderation of family and organizational support. Front Psychol. 2023;14:1084329. https://doi.org/10.3389/fpsyg.2023.1084329 Sato K, Kuroda S, Owan H. Mental health effects of long work hours, night and weekend work, and short rest periods. Soc Sci Med. 2020;246:112774. https://doi.org/10.1016/j.socscimed.2019.112774 Virtanen M, Stansfeld SA, Fuhrer R, Ferrie JE, Kivimäki M. Overtime work as a predictor of major depressive episode: a 5-year follow-up of the Whitehall II study. PLoS One. 2012;7(1):e30719. https://doi.org/10.1371/journal.pone.0030719 Amagasa T, Nakayama T. Relationship between long working hours and depression: a 3-year longitudinal study of clerical workers. J Occup Environ Med. 2013;55(8):863-72. https://doi.org/10.1097/JOM.0b013e31829b27fa Theorell T, Karasek RA. Current issues relating to psychosocial job strain and cardiovascular disease research. J Occup Health Psychol. 1996;1(1):9-26. https://doi.org/10.1037//1076-8998.1.1.9 du Prel JB, Koscec Bjelajac A, Franić Z, Henftling L, Brborović H, Schernhammer E, McElvenny DM, Merisalu E, Pranjic N, Guseva Canu I, Godderis L. The Relationship Between Work-Related Stress and Depression: A Scoping Review. Public Health Rev. 2024;45:1606968. https://doi.org/10.3389/phrs.2024.1606968 Madsen IEH, Nyberg ST, Magnusson Hanson LL,et al. Job strain as a risk factor for clinical depression: systematic review and meta-analysis with additional individual participant data. Psychol Med. 2017;47(8):1342-1356. https://doi.org/10.1017/S003329171600355X Hong Y, Zhang Y, Xue P, Fang X, Zhou L, Wei F, Lou X, Zou H. The Influence of Long Working Hours, Occupational Stress, and Well-Being on Depression Among Couriers in Zhejiang, China. Front Psychol. 2022;23(13):928928. https://doi.org/10.3389/fpsyg.2022.928928 Jung S, Shin YC, Lee MY, Oh KS, Shin DW, Kim ES, Kim MK, Jeon SW, Cho SJ. Occupational stress and depression of Korean employees: Moderated mediation model of burnout and grit. J Affect Disord. 2023;339(15):127-135. https://doi.org/10.1016/j.jad.2023.07.045 Hou C, Chen F, Wang J, Jin N, Li J, Zheng B, Ye M. Association between occupational stress, occupational burnout, and depressive symptoms among medical staff during COVID-19: A cross-sectional study in Chongqing, China. Work. 2024;78(2):305-315. https://doi.org/10.3233/WOR-230343 Hobfoll SE. Conservation of resources. A new attempt at conceptualizing stress. Am Psychol. 1989 Mar;44(3):513-24. https://doi.org/10.1037//0003-066x.44.3.513 Avey JB, Reichard RJ, Luthans F, Mhatre KH. Meta-analysis of the impact of positive psychological capital on employee attitudes, behaviors, and performance. Human Resource Development Quarterly. 2011;22:127-152. https://doi.org/10.1002/hrdq.20070 Sonnentag S, Fritz C. Recovery from job stress: The stressor-detachment model as an integrative framework. Organiz. Behav. 2015;36:S72–S103. https://doi.org/10.1002/job.1924 Ji J, Han Y, Li R, Jin H, Yin C, Niu L, Ying X, Gao Y, Ma Q. The role of effort-reward imbalance and depressive symptoms in the relationship between long working hours and presenteeism among Chinese village doctors: a moderated mediation model. BMC Psychiatry. 2023;23(1):497. https://doi.org/10.1186/s12888-023-04986-4 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6543738","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":463788336,"identity":"362609cc-5271-4df3-b08b-9e855029512d","order_by":0,"name":"Jin Wang","email":"","orcid":"","institution":"Institute of Occupational Health and Poison Control, Chinese Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Jin","middleName":"","lastName":"Wang","suffix":""},{"id":463788337,"identity":"5d32f263-5e12-44a7-bae3-1248b110d8cc","order_by":1,"name":"Xiao-Man Liu","email":"","orcid":"","institution":"Institute of Occupational Health and Poison Control, Chinese Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Xiao-Man","middleName":"","lastName":"Liu","suffix":""},{"id":463788338,"identity":"94dd4fc8-b251-4cc2-804e-a149484ef404","order_by":2,"name":"Shuang Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABC0lEQVRIiWNgGAWjYBACPmYGNjCDn4GxgRnEMACTbLi1sMG0SDYQrQUma3CAgQGiBSaOUws7+7PHPDV37Dbfbm58XLiDQd6cnffghx9ldgz8sxtwOIzH3Jjn2LPkbXcONhvPPMNguLOZL1my51wyg8SdA7i0sEnzsB1ONruR2CbN28aQYHCYx0Case0Ag4FEAg4t7M+kef4dTjaegdBi/Bu/FgYzoMrDdgYSCC1mBGzhMZOc23c4QeJGYrMx7xkJww1ALZZAv/BI3MCuhZ//+DOJN98O2/PPSH/4mHeHjbzB+TPGN4AhJsc/A7sWGEhsAJGMDRJwER686oHAngGihZC6UTAKRsEoGIkAALH6UbWnyNLCAAAAAElFTkSuQmCC","orcid":"","institution":"Institute of Occupational Health and Poison Control, Chinese Center for Disease Control and Prevention","correspondingAuthor":true,"prefix":"","firstName":"Shuang","middleName":"","lastName":"Li","suffix":""},{"id":463788339,"identity":"1750a7ec-7839-4551-868c-486980fc7ff5","order_by":3,"name":"Xia Wan","email":"","orcid":"","institution":"Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking, Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Xia","middleName":"","lastName":"Wan","suffix":""}],"badges":[],"createdAt":"2025-04-28 04:23:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6543738/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6543738/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83769975,"identity":"2920cf5c-4ab2-48e9-837f-d7c997c8e674","added_by":"auto","created_at":"2025-06-02 12:12:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":53865,"visible":true,"origin":"","legend":"\u003cp\u003eHypothetical model of the mediating effect of job stress on the relationship between working hours and depressive symptoms and the potential moderating effects on the paths\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6543738/v1/7ed310f12d6440f3083997f0.png"},{"id":83770235,"identity":"25f8ae35-0204-4b34-9138-a9c6a8277d14","added_by":"auto","created_at":"2025-06-02 12:20:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":44851,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 1 Moderating effect of Psychological Capital (PsyCap) on the relationship between job stress and depressive symptoms (n = 4 576)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6543738/v1/ebede2b08959f0fce8590ede.png"},{"id":99685541,"identity":"56dcc27c-5e28-4605-8e63-b9961a63c2b9","added_by":"auto","created_at":"2026-01-07 09:25:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1109781,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6543738/v1/6476bad0-258e-47f1-9871-ec061b25d008.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Relationships among working hours, job stress, psychological capital, and depressive symptoms among Chinese medical staff: a moderated mediation model","fulltext":[{"header":"Background","content":"\u003cp\u003eMental health, shaped by interconnected social, psychological, behavioral, and biological determinants, profoundly impacts individuals\u0026rsquo; daily functioning and occupational performance [1, 2, 3]. Approximately 15% of the global working-age population experiences depression, anxiety and other mental disorders [4], and medical staff, particularly in China, face heightened vulnerability due to heavy workloads, intense pressure, high risk, and frequent patient conflicts [6]. The COVID-19 pandemic further exacerbated these challenges, underscoring mental health as both a public health priority and a care-quality determinant [7, 8]. Studies have consistently shown that medical staff exhibit elevated depression rates [11, 12]. A recently published meta-analysis conducted during the pandemic revealed that the prevalence of depression among healthcare workers was as high as 29%, particularly among frontline workers who experienced cumulative risks from prolonged exposure to high-risk exposure and inadequate psychological support [13].\u003c/p\u003e\n\u003cp\u003eMany factors contribute to the emergence of depressive symptoms. For medical staff, demanding working hours and overwhelming workloads are significant contributors to adverse physical and mental health outcomes [14, 15]. Lang et al. [16] retrospectively investigated the psychological status of medical workers in China and reported a strong correlation between working hours and depressive symptoms. Through a systematic review and meta-analysis, Virtanen et al. [17] demonstrated a moderate association between long working hours and depressive symptoms in Asia, in contrast with a weaker association in Europe, suggesting that cultural and organizational differences likely mediate this disparity. However, the WHO/ILO Joint Estimates of the Work-related Burden of Disease and Injury conducted a meta-analysis that revealed insufficient evidence to definitively link long working hours to depression incidence [18]. This finding has sparked debate, with critics emphasizing methodological heterogeneity in study designs, measurement tools, and cultural contexts [19]. Others propose that multiple pathways\u0026mdash;including psychosocial characteristics, feelings of distress, sleep deprivation, and personality traits\u0026mdash;may explain inconsistencies in the literature [20, 21].\u003c/p\u003e\n\u003cp\u003eJob stress, characterized by excessive workload, time pressure, and an imbalance between high effort and low reward, exerts detrimental\u0026nbsp;effects on mental health. This stressor arises mainly from employees\u0026rsquo; interactions with adverse work environments, especially when harmful working conditions combine with long working hours [22]. Empirical evidence consistently indicates that extended work hours significantly exacerbate job stress levels, and chronic exposure to unmitigated job stress constitutes a significant risk factor for\u0026nbsp;the development of\u0026nbsp;mental disorders [23,\u0026nbsp;24]. For\u0026nbsp;example, a study based on the Korea Working Conditions Survey in 2014\u0026nbsp;revealed\u0026nbsp;that job stress partially explains the effects of long working hours on depressive symptoms [25]. These findings underscore job stress as a potential mediator in the pathway linking working hours to mental health deterioration.\u003c/p\u003e\n\u003cp\u003eConcurrently, the rise of positive psychology has shifted scholarly attention toward psychological resource-based interventions for work-related mental health issues. Psychological capital (PsyCap), defined as a developable positive psychological state, encompasses four core dimensions: self-efficacy, hope, optimism, and resilience [26]. Research has demonstrated the dual protective function of PsyCap against depressive symptoms through both direct and indirect mechanisms. Meta-analytic data confirmed a robust negative correlation (\u003cem\u003er\u0026nbsp;\u003c/em\u003e= -0.43) between PsyCap and depression, which was consistent across age groups and publication years [27]. Furthermore, PsyCap has been shown to enhance coping strategies, improve social support networks, and reduce perceived stress, all of which can mitigate the adverse effects of job stress on mental health [28].\u003c/p\u003e\n\u003cp\u003eWhile previous research has established a clear link between long working hours and depressive symptoms, the mechanisms underlying this association remain underexplored, especially in the context of Chinese healthcare settings. Furthermore, the potential role of job stress and psychological resources in mitigating these effects has not been thoroughly investigated within this population. Therefore, the current study aims to investigate the prevalence of depressive symptoms in Chinese medical staff and the associations among working hours, job stress, and depressive symptoms, with a specific focus on exploring whether PsyCap moderates these associations. On the basis of previous research, we developed a theoretical hypothesis model (Fig. 1) and propose the following hypotheses: (1) working hours are significantly correlated with depressive symptoms (path H1); (2) job stress mediates the relationship between working hours and depressive symptoms (paths H2 and H3); and (3) PsyCap moderates both the direct (path H5) and indirect effects (paths H4 and H6) of working hours on depressive symptoms.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eData and study subjects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe current cross-sectional study was carried out on the basis of the Program of Occupational Health Risk Assessment on Long Working Hours (OHRA-LWH), which was conducted by the National Institute of Occupational Health and Poison Control, Chinese for Disease Control and Prevention, and in collaboration with local institutes for the prevention of occupational disease. It is a three-year (from April 2021 to December 2023) national survey designed to assess the health risks associated with exposure to long working hours among key occupational groups in China, including IT engineers, medical staff, schoolteachers, manufacturing workers, couriers and take-out food deliverers. The program employs a stratified, multistage purposive sample strategy to obtain a systematic and representative sample of occupational groups. In the initial stage, 12 provincial cooperative units were recruited nationwide in accordance with the research protocol; in the second stage, each cooperative unit identified and purposively sampled 323 typical enterprises and institutions from various industries to conduct surveys on key occupational groups; in the final stage, stratified cluster sampling was conducted within each enterprise or institution, and individuals of each key occupational group (4 451 IT engineers, 13 641 medical staff, 8 133 school teachers, 13 317 manufacturing workers, 5 624 couriers and take-out food deliverers) were included if they participated in either occupational or routine health examinations during the investigation periods. The program requires participants to be working adults aged 18 years or older who voluntarily enroll.\u003c/p\u003e\n\u003cp\u003eConsidering the higher workload and requirements in tertiary public hospitals, the subjects of the current study were limited to medical staff, referring to doctors and nurses, from tertiary Grade A hospitals (n=5 178). Since the retirement ages in China are 60 and 55 for men and women, respectively, and to minimize health-related biases commonly observed after age 60, such as chronic diseases, we set the upper age limit of study participants at 60 years old. We also exclude individuals working fewer than 35 hours per week, as studies indicate that a significant proportion of those working fewer than standard hours do so due to preexisting health issues [29]. The survey was conducted by well-trained researchers via web-based self-administered questionnaires and sought information on a variety of topics, including demographic traits, working conditions, individual lifestyles, psychosocial and mental health status, illnesses and medical conditions. During the investigation, the researcher explained the purpose of the study to the subjects and obtained informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eWorking Hours\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the program, working hours were measured via the following two self-reported questions: (1) \u0026ldquo;How many hours per day, including overtime and part-time jobs, did you work over the past half year on average?\u0026rdquo; and (2) \u0026ldquo;how many days per week did you work over the past half year on average?\u0026rdquo; The number of average working hours per week (h/week) was calculated by multiplying the results of questions (1) and (2). Working hours were treated as continuous variables in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDepressive symptoms\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepressive symptoms were evaluated using the Chinese version of the Patient Health Questionnaire\u0026ndash;9 (PHQ\u0026ndash;9), a well-validated and efficient screening instrument that measures the presence and severity of depressive symptoms [30]. The PHQ\u0026ndash;9 is a self-report questionnaire consisting of nine items based on the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) criteria for assessing symptoms of depression experienced over the past two weeks. Each item is rated on a four-point Likert scale ranging from 0 (\u0026ldquo;not at all\u0026rdquo;) to 3 (\u0026ldquo;nearly every day\u0026rdquo;). The total score of the nine items was categorized into non or minimal (0--4), mild (5--9), moderate (10--14), moderately severe (15--19), and severe (20--27) depressive symptom groups, and increasing scores suggested greater severity of symptoms. In this study, participants with a sum score of 10 or above were defined as exhibiting depressive symptoms [31]. The Cronbach\u0026rsquo;s \u0026alpha; for the PHQ\u0026ndash;9 in this study was 0.898.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eJob\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003estress\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJob stress was evaluated using the Core Occupational Stress Scale (COSS), which was developed on the basis of key occupational groups in typical industries and has demonstrated strong reliability and validity in China [32]. The COSS comprises 17 items and four subscales: social support, organization and reward, demand and effort, and job control. Each item is rated on a five-point Likert scale ranging from 1 (\u0026ldquo;totally disagree\u0026rdquo;) to 5 (\u0026ldquo;totally agree\u0026rdquo;). The total score of the COSS is calculated by summing all items after reverse scoring of negatively keyed items. Higher COSS scores indicate greater severity of job stress. In this study, the Cronbach\u0026rsquo;s \u0026alpha; coefficient for the COSS score was 0.837.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePsychological capital\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePsyCap was evaluated using the Chinese version of the 24-item Psychological Capital Questionnaire (PCQ-24) [33]. The PCQ-24 consists of four dimensions: self-efficacy, optimism, resilience, and hope. Each item is rated on a six-point Likert scale ranging from 1 (\u0026ldquo;strongly disagree\u0026rdquo;) to 6 (\u0026ldquo;strongly agree\u0026rdquo;). The total score of the PCQ was calculated by summing all the items, with higher scores indicating greater levels of PsyCap. The PCQ has demonstrated robust reliability and validity across multiple samples [34]. The Cronbach\u0026rsquo;s alpha for this scale in the present study was 0.942, indicating excellent internal consistency reliability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCovariates\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo control for their impact as confounding factors, the following sociodemographic and health-related characteristics were added as covariates: region, gender, age, education level, income, marital status, occupation, years of work experience, night shift work status, smoking and drinking behavior, and frequency of physical activity.\u003c/p\u003e\n\u003cp\u003eThe participants were classified into three regions of China\u0026mdash;Eastern, Central, and Western\u0026mdash;on the basis of variations in economic development levels, resource availability, and natural conditions; four age groups\u0026mdash;\u0026le; 25 years, 26--35 years, 36--45 years, and \u0026gt; 45 years; three groups according to educational level\u0026mdash;college and below, undergraduate, and graduate and above; and three groups according to average monthly income (Chinese\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eyuan\u003c/em\u003e): less than 5000, 5000--8999, 9000 and more. Night shift work was defined as working at least two hours a day from 10 pm to 5 am and categorized as either yes or no. For individual lifestyle variables, self-reported smoking status was categorized as current smoker, former smoker, or never smoker; alcohol consumption behavior was defined as having consumed any alcoholic beverages in the past month and categorized as either \u0026ldquo;yes\u0026rdquo; or \u0026ldquo;no\u0026rdquo;; and physical activity (PA) levels were categorized as low, moderate, or high based on exercise frequency: less than 1\u0026ndash;3 times per month for low PA, 1\u0026ndash;2 times per week for moderate PA, and more than 3 times per week for high PA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData analysis was performed via SPSS (version 26.0 for Windows). The participants\u0026rsquo; characteristics were summarized by descriptive statistics (i.e., means, standard deviations, and percentages). Differences in depressive symptoms across sociodemographic subgroups were assessed through one-way ANOVA and independent samples \u003cem\u003et\u003c/em\u003e tests. Variables that were statistically significant in the univariate analysis were further analyzed using a multivariate linear regression model. Associations between continuous variables were evaluated using \u003cem\u003eSpearman\u0026rsquo;s\u003c/em\u003e correlation analysis. Statistically significant confounding factors identified through multivariate analysis were treated as covariates in subsequent analyses.\u003c/p\u003e\n\u003cp\u003eFollowing Zhao et al.\u0026rsquo;s procedure [35], a structured step-by-step approach was utilized to examine the mediating effect of job stress (M) on the relationship between working hours (X) and depressive symptoms (Y). A moderated mediation analysis [36] was conducted to investigate whether PsyCap (W) moderates the direct and indirect effects of X on Y. The mediation model (Model 4) and the moderated mediation model (Model 59) were implemented via the PROCESS macro plug-in for SPSS (version 3.5) [37]. The bootstrapping method (5000 bootstrapping samples) with bias-corrected 95% confidence intervals (CIs) was used to evaluate effect significance. Categorical variables were dummy-coded, and continuous variables were standardized to minimize scale heterogeneity. To further illustrate the moderating effect, simple slope analyses were conducted in accordance with Aiken and West\u0026rsquo;s procedures [38], and the conditional effects were evaluated at one standard deviation (\u003cem\u003eSD\u003c/em\u003e) above the mean, at the mean, and at one \u003cem\u003eSD\u003c/em\u003e below the mean for PsyCap, which served as the moderator variable of interest.\u003c/p\u003e\n\u003cp\u003eStatistical significance was determined at a \u003cem\u003eP\u003c/em\u003e value \u0026lt; 0.05 (two-tailed).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eParticipants\u0026rsquo; characteristics and depressive symptoms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter excluding participants who did not meet the inclusion criteria (n = 446) and those with invalid data (n=156), 4 576 valid questionnaires were obtained from 20 tertiary hospitals covering the eastern, middle and western regions of China. Table 1 shows the participants\u0026rsquo; sociodemographic and health-related characteristics. Overall, the participants in this study had a mean age of 35.23 years (\u003cem\u003eSD\u003c/em\u003e=8.71 years); most of them were females, accounting for 78.2% of the participants. The majority of the participants were undergraduates (57.5%) and had a monthly income of more than 9000 \u003cem\u003eyuan\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eAccording to the PHQ\u0026ndash;9 scores, 1552 (33.9%) of the participants were classified as having depressive symptoms; among them, the percentages of mild, moderate, moderately severe, and severe depressive symptoms were 48.8%, 21.9%, 9.4%, and 2.6%, respectively. The results of the independent sample \u003cem\u003et\u003c/em\u003e test and one-way ANOVA illustrate the significant differences in depressive symptoms among medical staff across various sociodemographic and health-related characteristics, including region, sex, age, education level, occupation, working years, night shift work, smoking behavior, risky drinking behavior, and frequency of physical activity (Table 1).\u003c/p\u003e\n\u003cp\u003eTable 1 \u0026nbsp;Participants\u0026rsquo; characteristics and univariate analysis of variables related to depressive symptoms among Chinese medical staff (n=4 576)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003ePHQ\u0026ndash;9 (\u003cem\u003eM\u003c/em\u003e \u0026plusmn; \u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003eF / \u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eRegion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eEastern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e3134 (68.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e8.74 \u0026plusmn; 4.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e6.792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eCentral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e753 (16.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e8.66 \u0026plusmn; 5.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eWestern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e689 (15.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e7.99 \u0026plusmn; 4.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1134 (24.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e9.07 \u0026plusmn; 5.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e3.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e3442 (75.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e8.47 \u0026plusmn; 4.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026le; 25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e556 (12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e8.40 \u0026plusmn; 4.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e5.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e26-35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2021 (44.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e8.56 \u0026plusmn; 4.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e36-45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1366 (29.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e8.00 \u0026plusmn; 4.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026gt; 45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e633 (13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e8.18 \u0026plusmn; 4.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eEducation level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eCollege and below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1049 (22.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e8.37 \u0026plusmn; 4.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e8.727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eUndergraduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2632 (57.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e8.51 \u0026plusmn; 4.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eGraduate and beyond\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e895 (19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e9.20 \u0026plusmn; 4.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eMonthly income (CNY)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026lt; 5000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e776 (17.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e8.46 \u0026plusmn; 4.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.553\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e5000-8999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1812 (39.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e8.68 \u0026plusmn; 4.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026ge; 9000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1988 (43.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e8.61 \u0026plusmn; 4.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1304 (28.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e8.64 \u0026plusmn; 4.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.688\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eUnmarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e3146 (68.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e8.59 \u0026plusmn; 4.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e128 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e8.96 \u0026plusmn; 4.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eOccupation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003ePhysicians\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2006 (43.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e8.86 \u0026plusmn; 4.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e2.994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eNurses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2570 (56.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e8.43 \u0026plusmn; 4.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eWorking years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026le; 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1235 (27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e8.36 \u0026plusmn; 4.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e3.843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e6-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e976 (21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e8.81 \u0026plusmn; 4.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e11-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e839 (18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e8.62 \u0026plusmn; 4.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e16-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e639 (14.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e9.16 \u0026plusmn; 5.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026gt; 20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e884 (19.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e8.36 \u0026plusmn; 4.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eNight shifts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1263 (27.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e7.72 \u0026plusmn; 4.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e- 8.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e3313 (72.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e8.96 \u0026plusmn; 4.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eSmoking status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e266 (5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e9.95 \u0026plusmn; 4.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e19.624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e182 (4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e9.96 \u0026plusmn; 5.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e4128 (90.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e8.47 \u0026plusmn; 4.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eDrinking habits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2253 (49.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e9.18 \u0026plusmn; 4.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e8.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2323 (50.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e8.06 \u0026plusmn; 4.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003ePA frequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1853 (40.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e9.23 \u0026plusmn; 4.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e31.439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1905 (41.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e8.40 \u0026plusmn; 4.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e819 (17.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e7.72 \u0026plusmn; 4.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eM\u003c/em\u003e, Mean; \u003cem\u003eSD\u003c/em\u003e, Standard deviation; CNY, \u003cem\u003eChinese Yuan\u003c/em\u003e; PA, Physical activity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe average working hours of the participants were 50.36 \u0026plusmn; 11.55 h/week, ranging from 35--112 h/week. The participants scored 8.61 \u0026plusmn; 4.80 on depressive symptoms, 44.35 \u0026plusmn; 8.97 on job stress, and 103.69 \u0026plusmn; 18.80 on psychological capital. Table 2 provides the \u003cem\u003eM\u003c/em\u003e, \u003cem\u003eSD\u003c/em\u003e, and \u003cem\u003eSpearman\u0026rsquo;s\u003c/em\u003e correlations between working hours, job stress, psychological capital and depressive symptoms. These results indicate that working hours were positively correlated with job stress (\u003cem\u003er\u003c/em\u003e = 0.276, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) and depressive symptoms (\u003cem\u003er\u003c/em\u003e = 0.175, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Moreover, job stress was positively correlated with depressive symptoms (\u003cem\u003er\u003c/em\u003e = 0.527, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). In contrast, negative correlations between PsyCap and job stress (\u003cem\u003er\u003c/em\u003e = -0.319, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) and depressive symptoms (\u003cem\u003er\u003c/em\u003e = -0.439, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) were found.\u003c/p\u003e\n\u003cp\u003eTable 2 \u0026nbsp;Correlations between working hours, job stress, psychological capital and depressive symptoms (n=4 576)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32px;\"\u003e\n \u003col\u003e\n \u003cli\u003eWorking hours\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e50.36 \u0026plusmn; 11.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32px;\"\u003e\n \u003col start=\"2\"\u003e\n \u003cli\u003ejob stress\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e44.35 \u0026plusmn; 8.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.276 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32px;\"\u003e\n \u003col start=\"3\"\u003e\n \u003cli\u003epsychological capital\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e103.69 \u0026plusmn; 18.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.060 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.319 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32px;\"\u003e\n \u003col start=\"4\"\u003e\n \u003cli\u003edepressive symptoms\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e8.61 \u0026plusmn; 4.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.175 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.527 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e- 0.439 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultivariate linear regression analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of the multiple linear regression analysis for depressive symptoms are presented in Table 3. Depressive symptoms were significantly associated with the following factors: residing in the western region (\u003cem\u003eB\u003c/em\u003e = - 0.608, \u003cem\u003ep\u003c/em\u003e = 0.003), having graduated and beyond the education level (\u003cem\u003eB\u003c/em\u003e = 0.648, \u003cem\u003ep\u003c/em\u003e = 0.017), working night shifts (\u003cem\u003eB\u003c/em\u003e = 1.270, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), never smoking (\u003cem\u003eB\u003c/em\u003e = - 1.144, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), engaging in risky drinking behavior (\u003cem\u003eB\u003c/em\u003e = - 0.097, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), and having moderate/high PA frequency (\u003cem\u003eB\u003c/em\u003e = - 0.963/- 1.170, both \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eTable 3 \u0026nbsp; Multiple linear regression analysis of variables related to depressive symptoms (n=4 576)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003etolerance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eVIF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eRegions (ref.\u0026nbsp;Eastern)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eCentral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e1.097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eWestern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e- 0.608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e- 2.986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e1.107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eGender (ref. Male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e1.674\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eUndergraduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e1.723\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eGraduate and beyond\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2.387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e2.419\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003ePosition (ref. Physicians)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e1.966\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eWorking years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2.698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e1.097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eNight shift (ref. None)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e7.630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e1.157\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eSmoking (ref. Current)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e1.636\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e- 1.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e- 3.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e1.971\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eDrinking (ref. Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e-0.900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e- 6.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e1.150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003ePA frequency (ref. Low)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e- 0.963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e- 6.240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e1.209\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e- 1.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e- 8.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e1.243\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e19.277 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eAdjusted \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: \u003cem\u003eB\u003c/em\u003e = unstandardized coefficient. \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMediation analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 4 presents the results of the mediation analysis examining the relationship between working hours and depressive symptoms via job stress. After adjusting for covariates, the analysis confirmed a significant association between working hours and job stress (path H2: \u003cem\u003eB\u003c/em\u003e = 0.212, 95% CI: 0.190, 0.234), as well as a positive effect of job stress on depressive symptoms (path H3: \u003cem\u003eB\u003c/em\u003e = 0.288, 95% CI: 0.275, 0.302). Analyses with bootstrap estimates (based on 5 000 bootstrap samples) indicated that the indirect effect through job stress was significant, with a point estimate of 0.061 and a bootstrapping 95% CI excluding zero (0.053, 0.069). However, the total effect of working hours became nonsignificant when job stress was included in the model (path H1: \u003cem\u003eB\u003c/em\u003e = 0.001, 95% CI: -0.011, 0.011). This pattern of results supports an indirect-only mediation model as described by Zhao et al. [35].\u003c/p\u003e\n\u003cp\u003eTable 4 \u0026nbsp;Mediation analysis between working hours, job stress and depressive symptoms (n=4 576)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eLLCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eULCI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome: Job stress\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eWorking hours (path H2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e18.834 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e75.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome: depressive symptoms\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eWorking hours (path H1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e- 0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eJob stress (path H3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e41.659 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e245.618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote:\u003c/p\u003e\n\u003cp\u003e** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01\u003c/p\u003e\n\u003cp\u003eCovariates include region, education level, night shifts, smoking status, drinking habits, and physical activity frequency.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModerated mediation analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 5 shows the findings of the moderated mediation analysis treating job stress as the mediator and PsyCap as a moderator in the association between working hours and depressive symptoms. The analysis revealed a significant moderating effect of PsyCap on the path from job stress to depressive symptoms (H6: \u003cem\u003eB\u003c/em\u003e = - 0.002, 95% CI: - 0.003, - 0.001). In contrast, PsyCap did not significantly moderate either the direct effect of working hours on depressive symptoms (H5:\u003cem\u003eB\u003c/em\u003e = - 0.001, 95% CI: - 0.001, 0.001) or the indirect effect of working hours on job stress (H4: \u003cem\u003eB\u003c/em\u003e = 0.001, 95% CI: - 0.001, 0.002).\u003c/p\u003e\n\u003cp\u003eTo visualize the interaction between job stress and PsyCap in relation to depressive symptoms, follow-up simple slope analysis (Figure 1) revealed that the line for higher PsyCap exhibited a steeper slope. These findings suggest that a higher level of PsyCap mitigates the impact of job stress on depressive symptoms.\u003c/p\u003e\n\u003cp\u003eTable 5 \u0026nbsp;Moderated mediation analysis between working hours, job stress, psychological capital and depressive symptoms (n = 4 576)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eLLCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eULCI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome: Job stress\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eWorking hours\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e18.680 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.213\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003ePsychological capital\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e- 0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e- 30.615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e- 0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e- 0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eWorking hours*psychological capital (path H4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e- 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e116.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome: depressive symptoms\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eWorking hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e- 0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eJob stress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e30.477 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.232\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003ePsychological capital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e- 0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e- 24.790 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e- 0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e- 0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eWorking hours*psychological capital (path H5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e- 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e- 0.716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e- 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eJob stress*psychological capital (path H6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e- 0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e- 4.430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e- 0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e- 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e267.739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote:\u003c/p\u003e\n\u003cp\u003e** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01\u003c/p\u003e\n\u003cp\u003eCovariates include region, education level, night shifts, smoking status, drinking habits, and physical activity frequency.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to investigate the prevalence of depressive symptoms and analyze the impact of job stress and PsyCap on the relationship between working hours and depressive symptoms using a moderated mediation model. To our knowledge, the study provides the first empirical evidence on how job stress mediates the association between working hours and depressive symptoms, and PsyCap moderates this pathway among Chinese medical staff. The findings demonstrated that working hours were positively correlated with depressive symptoms. Job stress was identified as an indirect-only mediator, whereas PsyCap was found to moderate the indirect effect between working hours and depressive symptoms. Specifically, PsyCap buffered the mediating effect of working hours on depressive symptoms via job stress.\u003c/p\u003e \u003cp\u003eThe study data revealed notable variations in depressive symptom prevalence among medical staff. Our findings demonstrate a 33.9% prevalence rate among physicians and nurses\u0026mdash;a figure surpassing both the general Chinese population (15.81%) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] and a nationwide hospital staff cohort (21.4%) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], despite the use of identical PHQ\u0026ndash;9 criteria. This discrepancy may be attributable to our exclusive focus on clinical practitioners bearing heavier clinical and research responsibilities, excluding other nonclinical or administrative staff. Regional studies have revealed substantial geographic heterogeneity in depression rates among Chinese medical staff. For example, a Heilongjiang Province study utilizing the Self\u0026ndash;Rating Depression Scale (SDS) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] identified depressive symptoms in 41.6% of physicians, whereas Yin et al. (2023) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] reported a markedly higher prevalence of 49.8% among frontline clinicians using the PHQ\u0026ndash;9 with a lower diagnostic threshold (cutoff\u0026thinsp;\u0026ge;\u0026thinsp;5). These discrepancies may be attributed to methodological variations in assessment tools (PHQ\u0026ndash;9 vs. SDS) and screening thresholds (\u0026ge;\u0026thinsp;5 vs. \u0026ge;10).\u003c/p\u003e \u003cp\u003eOur study confirmed that working hours were positively correlated with depressive symptoms, which is consistent with previous findings across various occupations [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Notably, this study reveals an indirect-only mediating effect of job stress on the relationship between working hours and depressive symptoms among Chinese medical staff. This suggests that working hours may not directly lead to depressive symptoms but rather act through the exacerbation of job stress. The job strain model posits that high psychological demands paired with high job control promote learning and a sense of mastery, thereby promoting beneficial health; conversely, high psychological demands coupled with low job control hinder learning, induce strain, and ultimately pose risks to health [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Similarly, it is conceivable that the impact of long working hours on depression risk may be more pronounced when accompanied by low job control and other adverse psychosocial factors, such as poor leadership quality or significant role conflict, than in situations without these additional factors [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. While our study identifies indirect-only mediation by job stress, the literature presents mixed evidence. Hong et al. demonstrated that working hours had direct and indirect effects on the depression of couriers in China through the mediating role of job stress [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. A Korean cohort study also revealed that job stress mediated only 20\u0026ndash;40% of the association between working hours and depression among employees [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. These discrepancies may arise from the fact that Chinese medical staff face dual pressures from extreme workloads and strained doctor‒patient relationships, potentially amplifying the mediating role of stress. A recent survey of 992 healthcare workers in China indicated that job burnout plays a mediating role in the relationship between long working hours and depressive symptoms [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The conceptual hierarchy suggests that job stress may act as a proximal mediator, whereas burnout represents a downstream consequence of chronic, unmitigated stress [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Our cross-sectional design likely captured the earlier stages of this cascade, where job stress fully accounts for the link between working hours and depression. In contrast, Yin et al. [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] identified burnout as a mediator that may reflect the sampling of participants with prolonged exposure to unrelieved stressors, allowing burnout to develop as a secondary pathway.\u003c/p\u003e \u003cp\u003eThe moderated mediation analysis provided nuanced insights into the role of PsyCap in occupational mental health. Specifically, our findings demonstrate a conditional indirect effect where PsyCap moderates the relationship between working hours and depressive symptoms through job stress (path H6). However, PsyCap did not have a moderating effect on either the direct association between working hours and depressive symptoms (path H5) or the initial indirect pathway from working hours to job stress (path H4). This pattern suggests that PsyCap functions primarily as a resilience reservoir during the critical transition from job stress to mental health deterioration rather than as a frontline buffer against work-hour exposure or initial stress generation. This pathway-specific moderating effect is supported by the theory of Conservation of Resources (COR) in occupational contexts, where PsyCap, as a personal resource caravan, likely becomes activated specifically during resource depletion phases following prolonged stress exposure [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. While excessive working hours may gradually drain psychological resources, PsyCap appears to mitigate subsequent depressive symptom development once stress accumulation surpasses individual coping thresholds [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. This explains why PsyCap's buffering effect only emerges at the job stress‒depression transition, where sustained resource depletion requires active replenishment through hope, efficacy, and optimism. The observed pattern is comparable with Ji et al.\u0026rsquo;s [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] findings on the interaction effect of long working hours and effort-reward imbalance (ERI) on depressive symptoms among Chinese village doctors. Our results suggest that PsyCap plays a critical role in intervention during the later stages of this pathway. This selective moderating effect aligns with and distinctively extends previous findings. Earlier research identified social support systems (both familial and organizational) as effective buffers that weaken the burnout‒depression link through similar pathway moderation [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The current results complement this evidence by demonstrating that internal psychological resources (PsyCap) and external support systems may operate through parallel protective mechanisms at different stages of stress. It can also be inferred from the slope that higher PsyCap and lower job stress attenuate depressive symptoms.\u003c/p\u003e \u003cp\u003eOur study highlights several potential practical implications. First, considering that long working hours influence depressive symptoms primarily through increased job stress levels, institutions should prioritize the implementation of workload management systems, such as rational shift scheduling and mandatory rest periods, along with stress-reduction programs specifically designed for medical staff. Second, the moderating role of PsyCap in the transition from job stress to depression indicates that resilience-building interventions targeting psychological resources such as efficacy, optimism, and hope can disrupt the pathway from chronic stress to mental health decline. Training programs incorporating cognitive‒behavioral techniques, mindfulness, or PsyCap development modules can equip medical staff with effective tools to mitigate the impact of stress.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, the cross-sectional design limits causal conclusions about the temporal relationships between variables. Longitudinal studies are needed to clarify whether long working hours increase job stress and subsequently worsen depressive symptoms. Second, self-reported data may introduce biases such as recall inaccuracies or socially desirable responses, particularly for sensitive outcomes such as depressive symptoms. Future research should use multiple assessment methods, including objective work-hour tracking and clinical evaluations of depression. Third, while PsyCap moderates the pathway from job stress to depressive symptoms, its limited effect on direct working hour impacts and initial stress generation suggests that unmeasured factors\u0026mdash;such as social support and leadership quality\u0026mdash;may also influence these stages. Future research models should incorporate additional mediators, such as burnout, and moderators, such as organizational resources, to comprehensively capture the full mechanism involved. Finally, the study did not account for potential confounding factors such as sleep quality, family responsibilities, and pandemic-related stressors, which may interact with working hours and mental health outcomes. Addressing these gaps could refine interventions targeting the complex interplay between occupational demands and psychological well-being in healthcare settings.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOwing to the significant imbalance in healthcare service demands and a shortage of medical workers in China, Chinese medical staff now face unprecedentedly heavy workloads that directly contribute to the development of mental health disorders. The psychological well-being of medical staff is linked to healthcare service quality and patient safety, making it a pressing public health priority. The present study reveals the increased prevalence of depressive symptoms in this population and confirms that extended working hours are positively associated with depressive symptoms. In particular, job stress and PsyCap act as mediators and moderators, respectively, influencing this association. Effective interventions for depressive symptoms in healthcare settings should integrate organizational reforms, such as reducing excessive working hours and alleviating job stress, with resilience-building programs that increase PsyCap and empower medical staff to better manage work-related stressors. These evidence-based findings offer actionable insights for policymakers and healthcare administrators to protect the mental health of medical workers, thereby enhancing the sustainability of China\u0026rsquo;s healthcare system.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in strict compliance with the World Medical Association Declaration of Helsinki and the National Guidelines on Ethical Review Measures for Life Sciences and Medical Research Involving Human Subjects.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthical approval was granted by the ethics committee of the National Institute of Occupational Health and Poison Control, Chinese Center for Disease Control and Prevention (Approval number: NIOHP202108). All the subjects provided e-signed informed consent, which could not be correlated with their questionnaires, and all the data were anonymous and untraceable.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. No individual-level data are presented within this publication.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the Institute of Occupational Health and Poison Control, China CDC, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Institute of Occupational Health and Poison Control, China CDC.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the the National Key Research and Development Program of China, Sub-project \u0026ldquo;Research on Key Technologies and Intervention Strategies for the Prevention and Control of Work-related Diseases and Occupational Injuries\u0026rdquo; (Grant No. 2022YFC2503205).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJW conceptualized the study, conducted the analysis, and wrote the first draft of the manuscript. XML managed data collection and was responsible for quality control. SL obtained funding and supervised the study. XW oversaw the analysis and contributed to interpreting the data. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our gratitude to all the participants in the project and acknowledge the assistance provided by the staff from the Guangdong Provincial Hospital for Occupational Disease Prevention and Treatment, the Shanghai Municipal Center for Disease Control and Prevention, the Second People\u0026rsquo;s Hospital of Heilongjiang Province, and the Third People\u0026rsquo;s Hospital of the Xinjiang Uygur Autonomous Region in participant recruitment.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTaubman DS, Parikh SV. Understanding and Addressing Mental Health Disorders: a Workplace Imperative. Curr Psychiatry Rep. 2023;25(10):455-463. https://doi.org/10.1007/s11920-023-01443-7\u003c/li\u003e\n\u003cli\u003eStrudwick J, Gayed A, Deady M, Haffar S, Mobbs S, Malik A, Akhtar A, Braund T, Bryant RA, Harvey SB. Workplace mental health screening: a systematic review and meta-analysis. Occup Environ Med. 2023;80(8):469-484. https://doi.org/10.1136/oemed-2022-108608\u003c/li\u003e\n\u003cli\u003eAfaf Khalid, Jawad Syed. Mental health and well-being at work: A systematic review of literature and directions for future research. Human Resource Management Review. 2024;34(1). https://doi.org/10.1016/j.hrmr.2023.100998\u003c/li\u003e\n\u003cli\u003eWHO guidelines on mental health at work. Geneva: World Health Organization; 2022.\u003c/li\u003e\n\u003cli\u003eKelloway EK, Dimoff JK, Gilbert S. Mental health in the workplace. Annu Rev Organ Psychol Organ Behav. 2023;10(1):363\u0026ndash;387. https://doi.org/10.1146/annurev-orgpsych-120920-050527\u003c/li\u003e\n\u003cli\u003eHe Y, Holroyd E, Koziol-McLain J. Understanding workplace violence against medical staff in China: a retrospective review of publicly available reports. BMC Health Serv Res. 2023;23(1):660-672. https://doi.org/10.1186/s12913-023-09577-3 \u003c/li\u003e\n\u003cli\u003eZhu H, Yang X, Xie S, Zhou J. Prevalence of burnout and mental health problems among medical staff during the COVID-19 pandemic: a systematic review and meta-analysis. BMJ Open. 2023;20;13(7):e061945. https://doi.org/10.1136/bmjopen-2022-061945\u003c/li\u003e\n\u003cli\u003eLiu Q, Luo D, Haase JE, Guo Q, Wang XQ, Liu S, Xia L, Liu Z, Yang J, Yang BX. The experiences of health-care providers during the COVID-19 crisis in China: a qualitative study. Lancet Glob Health. 2020;8(6):e790-e798. https://doi.org/10.1016/S2214-109X(20)30204-7\u003c/li\u003e\n\u003cli\u003eInstitute of Health Metrics and Evaluation. Global Health Data Exchange (GHDx). https://vizhub.healthdata.org/gbd-results/(Accessed 4 November 2024).\u003c/li\u003e\n\u003cli\u003eZhou J, Zhou J, Feng L, Feng Y, Xiao L, Chen X, Yang J, Wang G. The associations between depressive symptoms, functional impairment, and quality of life, in patients with major depression: undirected and Bayesian network analyses. Psychol Med. 2023 ;53(14):6446-6458. https://doi.org/10.1017/S0033291722003385\u003c/li\u003e\n\u003cli\u003ePappa S, Ntella V, Giannakas T, Giannakoulis VG, Papoutsi E, Katsaounou P. Prevalence of depression, anxiety, and insomnia among healthcare workers during the COVID-19 pandemic: A systematic review and meta-analysis. Brain Behav Immun. 2020;88:901-907. https://doi.org/10.1016/j.bbi\u003c/li\u003e\n\u003cli\u003eDragioti E, Tsartsalis D, Mentis M, Mantzoukas S, Gouva M. Impact of the COVID-19 pandemic on the mental health of hospital staff: An umbrella review of 44 meta-analyses. Int J Nurs Stud. 2022;131:104272. https://doi.org/10.1016/j.ijnurstu.2022.104272\u003c/li\u003e\n\u003cli\u003eHu N, Deng H, Yang H, Wang C, Cui Y, Chen J, Wang Y, He S, Chai J, Liu F, Zhang P, Xiao X, Li Y. The pooled prevalence of the mental problems of Chinese medical staff during the COVID-19 outbreak: A meta-analysis. J Affect Disord. 2022;303(15):323-330. https://doi.org/10.1016/j.jad.2022.02.045\u003c/li\u003e\n\u003cli\u003eVirtanen M, Ferrie JE, Singh-Manoux A, Shipley MJ, Stansfeld SA, Marmot MG, Ahola K, Vahtera J, Kivim\u0026auml;ki M. Long working hours and symptoms of anxiety and depression: a 5-year follow-up of the Whitehall II study. Psychol Med. 2011 Dec;41(12):2485-94. https://doi.org/10.1017/S0033291711000171\u003c/li\u003e\n\u003cli\u003eWong K, Chan AHS, Ngan SC. The Effect of Long Working Hours and Overtime on Occupational Health: A Meta-Analysis of Evidence from 1998 to 2018. Int J Environ Res Public Health. 2019;16(12):2102. https://doi.org/10.3390/ijerph16122102\u003c/li\u003e\n\u003cli\u003eLang Q, Liu X, He Y, Lv Q, Xu S. Association between Working Hours and Anxiety/Depression of Medical Staff during Large-Scale Epidemic Outbreak of COVID-19: A Cross-Sectional Study. Psychiatry Investig. 2020;17(12):1167-1174. https://doi.org/10.30773/pi.2020.0229\u003c/li\u003e\n\u003cli\u003eVirtanen M, Jokela M, Madsen IE, Magnusson Hanson LL, Lallukka T, Nyberg ST, et al. Long working hours and depressive symptoms: systematic review and meta-analysis of published studies and unpublished individual participant data. Scand J Work Environ Health. 2018;44(3):239-250. https://doi.org/10.5271/sjweh.3712\u003c/li\u003e\n\u003cli\u003eRugulies R, S\u0026oslash;rensen K, Di Tecco C, Bonafede M, Rondinone BM, Ahn S, et al. The effect of exposure to long working hours on depression: A systematic review and meta-analysis from the WHO/ILO Joint Estimates of the Work-related Burden of Disease and Injury. Environ Int. 2021;155:106629. https://doi.org/10.1016/j.envint.2021.106629\u003c/li\u003e\n\u003cli\u003eKivim\u0026auml;ki M, Virtanen M, Nyberg ST, Batty GD. The WHO/ILO report on long working hours and ischemic heart disease - Conclusions are not supported by the evidence. Environ Int. 2020;144:106048. https://doi.org/10.1016/j.envint.2020.106048\u003c/li\u003e\n\u003cli\u003eChoi E, Choi KW, Jeong HG, Lee MS, Ko YH, Han C, Ham BJ, Chang J, Han KM. Long working hours and depressive symptoms: moderation by gender, income, and job status. J Affect Disord. 2021;286:99-107. https://doi.org/10.1016/j.jad.2021.03.001\u003c/li\u003e\n\u003cli\u003eLiu X, Wang C, Wang J, Ji Y, Li S. Effect of long working hours and insomnia on depressive symptoms among employees of Chinese internet companies. BMC Public Health. 2021;21(1):1408. https://doi.org/10.1186/s12889-021-11454-9\u003c/li\u003e\n\u003cli\u003eLim JY, Kim GM, Kim EJ. Factors Associated with Job Stress among Hospital Nurses: A Meta-Correlation Analysis. Int J Environ Res Public Health. 2022 May 10;19(10):5792. https://doi.org/10.3390/ijerph19105792\u003c/li\u003e\n\u003cli\u003eBaek S-Uk ,Yoon J-H. Effect of long working hours on psychological distress among young workers in different types of occupation. Prev Med. 2024;179. https://doi.org/10.1016/j.ypmed.2023.107829\u003c/li\u003e\n\u003cli\u003evan der Molen HF, Nieuwenhuijsen K, Frings-Dresen MHW, de Groene G. Work-related psychosocial risk factors for stress-related mental disorders: an updated systematic review and meta-analysis. BMJ Open. 2020;10(7):e034849. https://doi.org/10.1136/bmjopen-2019-034849\u003c/li\u003e\n\u003cli\u003eYoon Y, Ryu J, Kim H, Kang CW, Jung-Choi K. Working hours and depressive symptoms: the role of job stress factors. Ann Occup Environ Med. 2018;;30:46. https://doi.org/10.1186/s40557-018-0257-5\u003c/li\u003e\n\u003cli\u003eClaude-H\u0026eacute;l\u0026egrave;ne M, Vanderheiden E. Contemporary Positive Psychology Perspectives and Future Directions. International Review of Psychiatry. 2020;32(7\u0026ndash;8):537\u0026ndash;41. https://doi.org/10.1080/09540261.2020.1813091\u003c/li\u003e\n\u003cli\u003eSong R, Song L. The relation between psychological capital and depression: a meta-analysis. Curr Psychol. 2024;43:18056\u0026ndash;18064. https://doi.org/10.1007/s12144-024-05626-0\u003c/li\u003e\n\u003cli\u003eWang MF, Shao P, Wu C, Zhang LY, Zhang LF, Liang J, Du J. The relationship between occupational stressors and insomnia in hospital nurses: The mediating role of psychological capital. Front Psychol. 2023;13:1070809. https://doi.org/10.3389/fpsyg.2022.1070809\u003c/li\u003e\n\u003cli\u003eKivim\u0026auml;ki M, Jokela M, Nyberg ST, Singh-Manoux A, Fransson EI, Alfredsson L, et al. Long working hours and risk of coronary heart disease and stroke: a systematic review and meta-analysis of published and unpublished data for 603,838 individuals. Lancet. 2015; 31;386(10005):1739-46. https://doi.org/10.1016/S0140-6736(15)60295-1\u003c/li\u003e\n\u003cli\u003eWang W, Bian Q, Zhao Y, Li X, Wang W, Du J, Zhang G, Zhou Q, Zhao M. Reliability and validity of the Chinese version of the Patient Health Questionnaire (PHQ\u0026ndash;9) in the general population. Gen Hosp Psychiatry. 2014;36(5):539-44. https://doi.org/10.1016/j.genhosppsych.2014.05.021\u003c/li\u003e\n\u003cli\u003eManea L, Gilbody S, McMillan D. Optimal cutoff score for diagnosing depression with the Patient Health Questionnaire (PHQ\u0026ndash;9): a meta-analysis. CMAJ. 2012;184(3):E191-6. https://doi.org/10.1503/cmaj.110829\u003c/li\u003e\n\u003cli\u003eWang J, Zhang QY, Chen HQ, Sun DY, Wang C, Liu XM, Sun YY, Li S, Yu SF. [Development of the Core Occupational Stress Scale for occupational populations in China]. Zhonghua Yu Fang Yi Xue Za Zhi. 2020;6;54(11):1184-1189. Chinese. https://doi.org/10.3760/cma.j.cn112150-20200319-00383\u003c/li\u003e\n\u003cli\u003eLuthans F, Youssef CM, Avolio BJ. Psychological Capital: Developing the Human Competitive Edge. Psychol Cap Dev Hum Compet Edge. 2007;1:1-256.\u003c/li\u003e\n\u003cli\u003eWang J, Yuan Z, He H, Jin M, Zeng L, Teng M, Ren Q. Development and psychometric testing of the psychological capital questionnaire for nurses. BMC Nurs. 2024;23:946. https://doi.org/10.1186/s12912-024-02633-1\u003c/li\u003e\n\u003cli\u003eZhao XS, Lynch J, Chen QM. Reconsidering Baron and Kenny: Myths and Truths about Mediation Analysis. J Consumer Res. 2010;37(2):197-206. https://doi.org/10.1086/651257\u003c/li\u003e\n\u003cli\u003eEdwards JR, Lambert LS. Methods for integrating moderation and mediation: A general analytical framework using moderated path analysis. Psychol. Methods. 2007;12:1-22.\u003c/li\u003e\n\u003cli\u003eHayes AF. Partial, conditional, and moderated moderated mediation: quantification, inference, and interpretation. Commun Monogr. 2018;85(1):4\u0026ndash;40. https://doi.org/10.1080/03637751.2017.1352100\u003c/li\u003e\n\u003cli\u003eAiken LS, West SG, Reno RR. Multiple regression: Testing and interpreting interactions. SAGE Publications. 1991.\u003c/li\u003e\n\u003cli\u003eLiu J, Wu J, Wang J, Chen S, Yin X, Gong Y. Prevalence and associated factors for depressive symptoms among the general population from 31 provinces in China: The utility of social determinants of health theory. J Affect Disord. 2024;347:269-277. https://doi.org/10.1016/j.jad.2023.10.134\u003c/li\u003e\n\u003cli\u003eMei Q, Li W, Feng H, Zhang J, Li J, Yin J, et al. Chinese hospital staff in anxiety and depression: Not only comfort patients but also should be comforted - A nationwide cross-sectional study. J Affect Disord. 2024;360:126-136. https://doi.org/10.1016/j.jad.2024.05.143\u003c/li\u003e\n\u003cli\u003eSun L, Zhang Y, He J, Qiao K, Wang C, Zhao S, et al. Relationship between psychological capital and depression in Chinese physicians: The mediating role of organizational commitment and coping style. Front Psychol. 2022;13:904447. https://doi.org/10.3389/fpsyg.2022.904447\u003c/li\u003e\n\u003cli\u003eYin C, Ji J, Cao X, Jin H, Ma Q, Gao Y. Impact of long working hours on depressive symptoms among COVID-19 frontline medical staff: The mediation of job burnout and the moderation of family and organizational support. Front Psychol. 2023;14:1084329. https://doi.org/10.3389/fpsyg.2023.1084329\u003c/li\u003e\n\u003cli\u003eSato K, Kuroda S, Owan H. Mental health effects of long work hours, night and weekend work, and short rest periods. Soc Sci Med. 2020;246:112774. https://doi.org/10.1016/j.socscimed.2019.112774\u003c/li\u003e\n\u003cli\u003eVirtanen M, Stansfeld SA, Fuhrer R, Ferrie JE, Kivim\u0026auml;ki M. Overtime work as a predictor of major depressive episode: a 5-year follow-up of the Whitehall II study. PLoS One. 2012;7(1):e30719. https://doi.org/10.1371/journal.pone.0030719\u003c/li\u003e\n\u003cli\u003eAmagasa T, Nakayama T. Relationship between long working hours and depression: a 3-year longitudinal study of clerical workers. J Occup Environ Med. 2013;55(8):863-72. https://doi.org/10.1097/JOM.0b013e31829b27fa\u003c/li\u003e\n\u003cli\u003eTheorell T, Karasek RA. Current issues relating to psychosocial job strain and cardiovascular disease research. J Occup Health Psychol. 1996;1(1):9-26. https://doi.org/10.1037//1076-8998.1.1.9\u003c/li\u003e\n\u003cli\u003edu Prel JB, Koscec Bjelajac A, Franić Z, Henftling L, Brborović H, Schernhammer E, McElvenny DM, Merisalu E, Pranjic N, Guseva Canu I, Godderis L. The Relationship Between Work-Related Stress and Depression: A Scoping Review. Public Health Rev. 2024;45:1606968. https://doi.org/10.3389/phrs.2024.1606968\u003c/li\u003e\n\u003cli\u003eMadsen IEH, Nyberg ST, Magnusson Hanson LL,et al. Job strain as a risk factor for clinical depression: systematic review and meta-analysis with additional individual participant data. Psychol Med. 2017;47(8):1342-1356. https://doi.org/10.1017/S003329171600355X\u003c/li\u003e\n\u003cli\u003eHong Y, Zhang Y, Xue P, Fang X, Zhou L, Wei F, Lou X, Zou H. The Influence of Long Working Hours, Occupational Stress, and Well-Being on Depression Among Couriers in Zhejiang, China. Front Psychol. 2022;23(13):928928. https://doi.org/10.3389/fpsyg.2022.928928\u003c/li\u003e\n\u003cli\u003eJung S, Shin YC, Lee MY, Oh KS, Shin DW, Kim ES, Kim MK, Jeon SW, Cho SJ. Occupational stress and depression of Korean employees: Moderated mediation model of burnout and grit. J Affect Disord. 2023;339(15):127-135. https://doi.org/10.1016/j.jad.2023.07.045\u003c/li\u003e\n\u003cli\u003eHou C, Chen F, Wang J, Jin N, Li J, Zheng B, Ye M. Association between occupational stress, occupational burnout, and depressive symptoms among medical staff during COVID-19: A cross-sectional study in Chongqing, China. Work. 2024;78(2):305-315. https://doi.org/10.3233/WOR-230343\u003c/li\u003e\n\u003cli\u003eHobfoll SE. Conservation of resources. A new attempt at conceptualizing stress. Am Psychol. 1989 Mar;44(3):513-24. https://doi.org/10.1037//0003-066x.44.3.513\u003c/li\u003e\n\u003cli\u003eAvey JB, Reichard RJ, Luthans F, Mhatre KH. Meta-analysis of the impact of positive psychological capital on employee attitudes, behaviors, and performance. Human Resource Development Quarterly. 2011;22:127-152. https://doi.org/10.1002/hrdq.20070\u003c/li\u003e\n\u003cli\u003eSonnentag S, Fritz C. Recovery from job stress: The stressor-detachment model as an integrative framework. Organiz. Behav. 2015;36:S72\u0026ndash;S103. https://doi.org/10.1002/job.1924\u003c/li\u003e\n\u003cli\u003eJi J, Han Y, Li R, Jin H, Yin C, Niu L, Ying X, Gao Y, Ma Q. The role of effort-reward imbalance and depressive symptoms in the relationship between long working hours and presenteeism among Chinese village doctors: a moderated mediation model. BMC Psychiatry. 2023;23(1):497. https://doi.org/10.1186/s12888-023-04986-4\u003c/li\u003e\n\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":"Depressive symptoms, Working hours, Job stress, Psychological capital, Medical staff","lastPublishedDoi":"10.21203/rs.3.rs-6543738/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6543738/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAlthough several studies have revealed an association between long working hours and depressive symptoms, the mechanisms underlying this association are not entirely clear. This study examined the complex interplay among working hours, job stress, psychological capital, and depressive symptoms among Chinese medical staff via a moderated mediation model.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUtilizing data from the National Occupational Health Risk Assessment on Long Working Hours program (2021\u0026ndash;2023), this cross-sectional study focused on medical staff, including both doctors and nurses, from tertiary Grade A hospitals. Web-based questionnaires were employed to measure variables using the Patient Health Questionnaire-9, Core Occupational Stress Scale, and 24-item Psychological Capital Questionnaire. Data analyses were conducted via SPSS 26.0 and Hayes\u0026rsquo; PROCESS macro (Model 4 for mediation; Model 59 for moderated mediation) with 5,000 bootstrap samples to assess direct/indirect effects between working hours, job stress, psychological capital, and depressive symptoms.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 4,576 participants from 20 tertiary hospitals with valid questionnaires were included. The prevalence of depressive symptoms was 33.9%. Working hours (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.276, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and job stress (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.175, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were positively correlated with depressive symptoms, whereas psychological capital was negatively correlated with both job stress (\u003cem\u003er\u003c/em\u003e = -0.319, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and depressive symptoms (\u003cem\u003er\u003c/em\u003e = -0.439, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The findings indicate that job stress has an indirect-only mediating effect on the relationship between working hours and depressive symptoms. Additionally, PsyCap moderates the indirect pathway between working hours and depressive symptoms via job stress. Specifically, higher levels of psychological capital weaken the impact of job stress on depressive symptoms.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eWorking hours are positively associated with depressive symptoms among Chinese medical staff, with job stress mediating and psychological capital moderating this association. Therefore, addressing medical staff\u0026rsquo;s mental health requires effective workload management systems and resilience-building interventions aimed at improving psychological resources.\u003c/p\u003e","manuscriptTitle":"Relationships among working hours, job stress, psychological capital, and depressive symptoms among Chinese medical staff: a moderated mediation model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-02 12:12:36","doi":"10.21203/rs.3.rs-6543738/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":"6972ef8e-26df-4011-a5a9-19c619b54326","owner":[],"postedDate":"June 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-07T09:25:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-02 12:12:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6543738","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6543738","identity":"rs-6543738","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}