Dual Pressure of Low Income and High Workload: Impact on Mental Health and Sleep Quality Among Chinese Medical Residents, and the Buffering Role of Social Support

Dual Pressure of Low Income and High Workload: Impact on Mental Health and Sleep Quality Among Chinese Medical Residents, and the Buffering Role of Social Support | 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 Dual Pressure of Low Income and High Workload: Impact on Mental Health and Sleep Quality Among Chinese Medical Residents, and the Buffering Role of Social Support Xingtao Zhao, Xin Wang, Xiajin Ren This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8598823/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Objective: This study aimed to assess the mental health status (depression, anxiety, stress, insomnia) of Chinese medical residents facing a unique "low income + high workload" dual pressure, identify key influencing factors, and explore the differential buffering effects of emotional and practical social support. Methods: A cross-sectional survey was conducted among 469 medical residents (28.26% from internal medicine, 19.8% from surgery, 17.5% from emergency medicine, and 34.4% from other departments) using standardized scales (DASS-21, ISI, MSPSS, WHOQOL-BREF). Descriptive statistics, factor analysis, multiple linear regression, interaction effect tests, and structural equation modeling (SEM) were performed using SPSS 26.0, AMOS 24.0, and R 4.3.0 (packages: ggplot2 3.4.4, lavaan 0.6-16, factoextra 1.0.7). Results: The prevalence of moderate-to-severe anxiety, depression, and clinical insomnia was 15.8%, 11.9%, and 6.1%, respectively. Weekly working hours (β=0.193, p=0.007), monthly on-call frequency (β=0.126, p=0.015), and monthly consumption (β=0.143, p=0.003) were independent risk factors for insomnia. Social support significantly buffered the negative impact of work stress on mental health (interaction term β=-0.089, p<0.05), with emotional and practical support exerting similar protective effects (family support: β=-0.062; friend support: β=-0.058). The SEM showed good fit (χ²/df=2.371, RMSEA=0.054, CFI=0.928, TLI=0.917, SRMR=0.048), confirming the paths: work stress→mental health (β=0.412, p<0.001), social support→mental health (β=-0.326, p<0.001), and mental health→quality of life (β=0.684, p<0.001). Conclusion: Chinese medical residents face severe mental health challenges driven by dual pressure. Reducing work burden, improving economic support, and constructing multi-dimensional social support systems are crucial for workplace mental health promotion, providing actionable empirical evidence for optimizing China’s resident standardized training system and public health policy-making. Medical residents Mental health Dual pressure Social support Sleep quality Occupational health Cross-sectional study Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction 1.1 Research Background Mental health crises among medical trainees have become a global public health concern, with the prevalence of depression and anxiety among medical residents being 2–3 times higher than that of the general population [ 1 , 2 ]. This not only threatens the physical and mental well-being of residents but also imposes potential risks on clinical work quality and medical safety [ 3 ]. China’s standardized training system covers over 100,000 residents annually [ 4 ], and while global studies have confirmed work overload and irregular schedules as core risk factors [ 5 , 6 ], the mental health challenges faced by Chinese medical residents exhibit distinct characteristics rooted in this system. Chinese medical residents undergo intensive training characterized by a unique "low income + high workload" dual pressure pattern. The Report on the Current Situation and Salary Consumption of Standardized Training for Resident Physicians in China (2023) pointed out that the income level of Chinese standardized training residents is generally low relative to their high-intensity clinical workloads [ 4 ]. Meanwhile, relevant studies have indicated that Chinese residents are exposed to frequent night shifts and prolonged working hours, which further exacerbate their work-related burden [ 6 ]. In contrast, research on medical residents in Europe and America has mainly focused on the impacts of medical disputes and career burnout rather than economic pressure [ 7 ]. This prominent contradiction between low income and heavy work burden has become an understudied hidden stressor in existing literature. Existing studies on Chinese medical residents’ mental health have mainly focused on single-factor associations (e.g., work stress → depression) [ 8 ], lacking systematic exploration of the multi-dimensional structure of mental health and the interactive mechanisms between factors. Three key research gaps remain: first, few studies have distinguished the differential buffering effects of emotional support and practical support, and workplace support (from colleagues/supervisors) has shown limited protective effects in the Chinese hierarchical clinical culture [ 9 ]; second, the chain effect of sleep quality between dual pressure and mental health is not fully clarified [ 10 ]; third, the interactive role of economic pressure with work stress and social support has not been adequately verified [ 11 ]. This study focuses on the mediating role of sleep quality and the interaction effect of economic pressure, addressing the above gaps. 1.2 Research Questions & Hypotheses Research Questions What is the overall mental health status of Chinese medical residents, including the prevalence of depression, anxiety, stress, and clinical insomnia under the dual pressure pattern? What are the key influencing factors of mental health (work-related, economic, social support, etc.) among this group? Do emotional support and practical support exert differential buffering effects on the relationship between dual pressure and mental health? What is the structural relationship between dual pressure, social support, mental health, and quality of life? Research Hypotheses H1: Higher dual pressure (workload + economic pressure) is positively associated with depression, anxiety, and insomnia. H2: Higher social support (emotional and practical) is negatively associated with mental health problems. H3: Social support (emotional and practical) moderates the association between dual pressure and mental health. H4: Mental health plays a mediating role between dual pressure/social support and quality of life. This study aims to fill the above gaps by integrating multiple statistical methods (factor analysis, interaction effect tests, structural equation modeling) to comprehensively analyze the mental health status and influencing mechanisms of Chinese medical residents. The findings are expected to provide targeted empirical evidence for optimizing the standardized training system and promoting occupational mental health in the medical field. 2. Literature Review 2.1 Mental Health Status of Medical Residents Global studies have consistently shown that medical residents are a high-risk group for mental health issues. Multiple studies have confirmed that the mental health risk of medical residents is significantly higher than that of the general population [ 1 , 2 ]. In China, relevant studies have reported similar results: He et al. (2025) conducted a cross-sectional survey of 453 standardized training residents in Chinese tertiary hospitals, revealing that sleep disturbances (SD) exert negative impacts on residents’ mental health, with psychological resilience (PR) partially mediating the association between SD and suicidal ideation (SI), as well as between SD and life satisfaction (LS) [ 12 ]. Qin et al. (2023) further indicated that healthcare workers facing heavier workloads are more prone to depression, and this association is particularly prominent among residents in high-pressure departments such as internal medicine and emergency medicine [ 10 ]. The high incidence of mental health problems among medical residents is closely linked to their unique training environment, including excessive clinical workload, high risk of medical errors, and stringent performance assessment requirements [ 5 , 6 ]. Specifically, An et al. (2024) confirmed that heavy clinical tasks and strict training evaluations are key factors contributing to job burnout among standardized training residents [ 6 ], while Fatima et al. (2021) noted that the pressure of avoiding medical errors during training further exacerbates residents’ psychological burden [ 5 ]. 2.2 Work Stress and Mental Health Work stress is a core factor affecting the mental health of medical residents. The main stressors include heavy clinical workload, high pressure of avoiding medical errors, and stringent training evaluations [ 5 , 6 ]. Shanafelt et al. (2012) revealed that physicians with poor work-life balance showed significantly higher levels of emotional exhaustion, a core dimension of burnout, compared with those with balanced work and life [ 7 ]. For resident physicians, night shifts are a key contributor to sleep disturbances, and accumulating evidence has confirmed that sleep disorders can further mediate the negative impact of work stress on mental health [ 10 ]. In China, the work stress of standardized training residents is also not optimistic: An et al. (2024) identified heavy clinical tasks and strict training assessments as key factors leading to job burnout among Chinese residents [ 6 ]. Meanwhile, Qin et al. (2023) found that heavier workloads were positively associated with higher depressive symptoms among Chinese healthcare workers, suggesting that overwork is an important driver of mental health problems in this population [ 10 ]. Additionally, Gong et al. (2014) noted that high work risk and long working hours were common risk factors for anxiety and depressive symptoms among Chinese physicians [ 8 ]. 2.3 The Impact of Social Support Social support plays a crucial buffering role in the relationship between stress and mental health, as proposed by the Stress-Buffering Model [ 13 ]. Cohen et al. (2007) updated the model, suggesting that emotional support can regulate the hypothalamic-pituitary-adrenal axis function, reducing the physiological response to stress [ 13 ]. For medical residents, social support from family, friends, and colleagues can alleviate work pressure and negative emotions [ 9 ]. However, existing studies often ignore the differences between emotional support and practical support, and the protective effect of workplace support (colleagues, supervisors) is less significant in the Chinese context [ 9 ]. Gong et al. (2014) attributed this to the high-pressure clinical environment and strict hierarchical relationships in Chinese hospitals, which hinder the formation of effective peer support networks among medical staff [ 8 ]. 2.4 Income Level and Mental Health Economic pressure is an important hidden factor affecting the mental health of medical residents. Viseu et al. (2018) confirmed that economic stress factors were positively correlated with stress, anxiety and depression symptoms among medical staff, and social support could moderate this adverse association [ 11 ]. For Chinese standardized training residents, this issue is particularly prominent: the Report on the Current Situation and Salary Consumption of Standardized Training for Resident Physicians in China (2023) pointed out that their income level is generally low relative to high-intensity clinical workloads, which directly induces economic pressure and further exacerbates psychological burden [ 4 ]. This is consistent with the conclusion of Viseu et al. (2018), indicating that economic stress is a universal risk factor for mental health problems among medical practitioners across different contexts. 2.5 Summary and Research Gaps Existing studies have confirmed the high incidence of mental health problems (e.g., depression, anxiety, and burnout) among medical residents, with work stress, social support, and economic factors identified as key influencing variables [ 5 , 9 , 11 ]. However, several research gaps remain to be addressed: 1) Lack of systematic analysis of the mental health status, particularly failing to reveal the potential dimensional structure and comorbidity patterns of depression, anxiety, and insomnia among this population [ 12 ]; 2) Insufficient exploration of the interaction mechanisms between multiple factors, especially the differential buffering effects of different types of social support (e.g., emotional vs. practical support) [ 9 ]; 3) Few studies have adopted structural equation modeling to clarify the chain mediating or moderating relationships between work stress, social support, mental health outcomes, and quality of life [ 12 ]. To fill these gaps, this study integrates multiple statistical methods to comprehensively explore the mental health status and influencing factors of Chinese medical residents, aiming to provide empirical evidence for targeted intervention strategies. 3. Methods 3.1 Study Design & Participants Design : Cross-sectional quantitative study. Participants : Convenience sampling was used to recruit residents from a tertiary hospital in China from January to April 2024. Inclusion criteria : ≥18 years old, enrolled in standardized training for more than 3 months. Exclusion criteria : History of severe mental illness (diagnosed by a psychiatrist), incomplete questionnaire responses. Sample Size : A total of 500 questionnaires were distributed, 453 were initially valid, and 16 additional valid responses were included after further data cleaning (missing key items ≤ 10%), resulting in a final sample of 469 (response rate 93.8%). Sample size was calculated based on a previous study reporting a 20% prevalence of anxiety among medical residents [ 2 ]. Setting α = 0.05 and power = 0.8, the minimum sample size was estimated to be 385 using the formula n = Z²π(1-π)/d². This study included 469 valid responses, meeting the statistical requirement. The sample included 277 females (59.1%) and 192 males (40.9%), aged 22–40 years (mean 25.4 ± 2.3 years). Among them, 80.35% were professional master's degree residents, 50.99% were in the second year of training, and the department distribution was: internal medicine (28.26%), surgery (8.83%), emergency medicine (3.31%), orthopedics (11.26%), obstetrics & gynecology (7.28%), anesthesiology (7.06%), neurology (4.42%), radiology (4.19%), and other departments (33.07%) (see Table 1 for detailed distribution). 3.2 Measures The socio-demographic and work-related questionnaire used in this study was developed specifically for investigating the dual pressure of low income and high workload among Chinese medical residents. It was not adapted from any previously published questionnaires, as no existing tools fully align with the research focus on residency training-related stressors in the Chinese context. The questionnaire consists of two parts: Part A includes socio-demographic and professional information, and Part B covers work-related factors and subjective perceptions. All items were designed following standardized questionnaire development principles, including specific language framing, mutually exclusive response categories, and clear skip patterns. The English version of the questionnaire is available as Supplementary File 3. Assessment Tools Information Table Category Tools Dimensions & Scoring Reliability (Cronbach’s α) Reference Mental Health DASS − 21 3 dimensions (depression/anxiety/stress), 7 items each, 4-point scale (0 = never to 3 = always) 0.89 [ 14 ] Lovibond SH, Lovibond PF. The structure of negative emotional states: Comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. Behav Res Ther. 1995;33(3):335–343. doi: 10.1016/0005-7967(94)00075-L . Mental Health ISI 7 items, 5-point scale (0 = none to 4 = very severe); ≥15 = clinical insomnia 0.82 [ 15 ]Bastien CH, Vallières A, Morin CM. Insomnia Severity Index (ISI): Psychometric properties in primary insomnia and secondary insomnia. Sleep Med. 2001;2(4):297–307. doi: 10.1016/S1389-9457(01)00062-4 . Social Support MSPSS 3 dimensions (family/friend/significant other support), 4 items each, 7-point scale [ 22 ] 0.91 [ 16 ]Zimet GD, Dahlem NW, Zimet SG, Farley GD. The Multidimensional Scale of Perceived Social Support. J Pers Assess. 1988;52(1):30–41. doi: 10.1080/00223891.1988.9924518 . Work-related Factors Self-designed questionnaire Weekly working hours, monthly on-call frequency, self-reported work stress (low/medium/high) . - [ 17 ]See Supplementary File 3 for the English version. Socio-economic Factors Self-designed questionnaire Gender, age, education, monthly income, monthly consumption, marital status - [ 17 ]See Supplementary File 3 for the English version. Quality of Life WHOQOL - BREF 4 dimensions (physical/psychological/social/environmental), 26 items, 5-point scale 0.88 [ 18 ]WHO. The World Health Organization Quality of Life Assessment (WHOQOL): Development and general psychometric properties. Soc Sci Med. 1998;46(12):1569–1585. doi: 10.1016/S0277-9536(98)00085-6 . The self-designed questionnaires for work-related and socio-economic factors were developed specifically for this study to adapt to the characteristics of Chinese medical residents. The English version is provided as Supplementary File 3, and the Chinese version (used for data collection) is available upon reasonable request from the corresponding author. 3.3 Data Collection Ethical approval was obtained from the Ethics Committee of The Second Hospital of Shanxi Medical University on January 15, 2024 (Approval No.: 2024-012). The questionnaire was distributed online via the Wenjuanxing platform, and participants completed it anonymously after reading the informed consent form. The survey took approximately 15 minutes to complete, and data were exported and cleaned to eliminate invalid responses (missing key items > 10%). 3.4 Statistical Analysis Analysis Information Table Analysis Type Purpose Software/Tools Descriptive Statistics Describe sample characteristics and variable distributions (frequency, mean, SD) SPSS 26.0 Factor Analysis Extract potential factors of mental health/social support (KMO/Bartlett’s test) SPSS 26.0, R (factoextra package) Correlation Analysis Analyze relationships between work stress, social support, and mental health SPSS 26.0, R (corrplot package) Multiple Linear Regression Identify independent predictors of mental health outcomes (with multicollinearity test: VIF < 3) SPSS 26.0, R (sjPlot package) Interaction Effect Test Test moderating role of social support (cross-product terms + simple slope) R (interactions package) Structural Equation Model Verify causal paths between variables (χ²/df, RMSEA, CFI, TLI, SRMR) AMOS 24.0, R (lavaan package) 3.5 R Code for Data Analysis and Visualization All statistical analyses (factor analysis, correlation analysis, multiple linear regression, interaction effect test, structural equation modeling) and data visualization (Figs. 1 – 3 ) were performed using R 4.3.0 with packages including ggplot2 3.4.4, lavaan 0.6–16, factoextra 1.0.7, and interactions 1.1.5; the complete and reproducible R code (with detailed annotations) is provided in Supplementary File 1, which can be directly run to reproduce the results. 4. Results 4.1 Sample Characteristics The sample was predominantly female (59.10%) and aged 25–26 years (50.30%). Most residents had a bachelor’s degree (59.91%), were professional master’s degree candidates (80.35%), and were in the second year of training (50.99%). Regarding department distribution: internal medicine (133, 28.26%), surgery (41, 8.83%), orthopedics (53, 11.26%), obstetrics & gynecology (34, 7.28%), anesthesiology (33, 7.06%), emergency medicine (15, 3.31%), neurology (21, 4.42%), radiology (20, 4.19%), and other departments (155, 33.07%). Regarding work-related characteristics, the average weekly working time was 51.75 ± 15.04 hours, 92.8% had night shifts, and 65.90% were on-call 3–5 times per month. Economically, 83.89% had a monthly income of < 2000 RMB, and 60.04% had a monthly consumption of 1000–1999 RMB. Detailed characteristics are shown in Table 1 . Table 1 Sample Characteristics (n = 469) Category Details Frequency (n) Percentage (%) Gender Female 277 59.10 Male 192 40.90 Age (years) 22–24 119 25.40 25–26 236 50.30 ≥ 27 114 24.30 Educational Background Bachelor 281 59.91 Master 187 39.87 Doctor 1 0.22 Training Grade First year 113 24.10 Second year 239 50.99 Third year 117 24.91 Department Internal Medicine 133 28.26 Surgery 41 8.83 Orthopedics 53 11.26 Obstetrics & Gynecology 34 7.28 Anesthesiology 33 7.06 Emergency Medicine 15 3.31 Neurology 21 4.42 Radiology 20 4.19 Other Departments 155 33.07 Monthly Income (RMB) < 2000 398 83.89 2000–3999 47 10.02 ≥ 4000 24 5.12 Weekly Working Hours Mean ± SD 51.75 ± 15.04 - Monthly On-call Frequency 1–2 times 94 20.04 3–5 times 309 65.90 ≥ 5 times 66 14.07 Work Stress Perception Low 37 7.90 Medium 308 65.70 High 124 26.40 Note: All data are based on 469 valid responses; percentages are rounded to two decimal places. 4.2 Mental Health Status The mean scores of DASS-21 dimensions were: depression (5.08 ± 7.36), anxiety (5.53 ± 6.99), stress (6.63 ± 8.06). The prevalence of moderate-to-severe depression, anxiety, and stress was 11.9%, 15.8%, and 3.6%, respectively. The mean ISI score was 5.53 ± 5.09, with 25.2% having subclinical insomnia and 6.1% having clinical insomnia (moderate-severe). Detailed severity distributions are shown in Table 2 and Fig. 1 . Table 2 Mental Health Status Distribution (n = 469) Outcome Outcome Mean ± SD Severity Classification Frequency (n) Percentage (%) DASS − 21 Depression 5.08 ± 7.36 Mild 394 84.0 Moderate 56 11.9 Severe 19 4.1 DASS − 21 Anxiety 5.53 ± 6.99 Mild 363 77.4 Moderate 74 15.8 Severe 32 6.8 DASS − 21 Stress 6.63 ± 8.06 Mild 416 88.7 Moderate 36 7.7 Severe 17 3.6 ISI Score 5.53 ± 5.09 No clinical significance 327 69.7 Subclinical insomnia 118 25.2 Clinical insomnia (moderate - severe) 24 6.1 4.3 Factor Analysis Results 4.3.1 Mental Health Factors (DASS-21 + ISI) KMO = 0.892 (> 0.8), Bartlett’s sphericity test χ²=5862.371, df = 300, p 1, cumulative variance explanation rate = 69.76%: Factor 1 (emotional depression): Included items such as "feeling depressed", "no expectation for the future", and "lack of enthusiasm" (factor loading: 0.78–0.86). Factor 2 (anxiety-somatic symptoms): Included "nervousness", "dyspnea", and "abnormal heartbeat" (factor loading: 0.72–0.81). Factor 3 (sleep disturbance): Included "difficulty falling asleep", "difficulty maintaining sleep", and "early awakening" (factor loading: 0.75–0.83). The Cronbach’s α coefficients of the three factors were 0.876, 0.821, and 0.798, respectively, with composite reliability (CR) > 0.8 and average variance extracted (AVE) > 0.5, indicating good reliability and validity (Supplementary Table 1). 4.3.2 Social Support Factors (MSPSS) KMO = 0.865, Bartlett’s sphericity test χ²=3217.542, df = 66, p < 0.001. Two factors were extracted, cumulative variance explanation rate = 72.34%: Factor 1 (emotional support): Included "family emotional support" and "sharing joys and sorrows with friends" (factor loading: 0.80–0.87). Factor 2 (practical support): Included "family practical help" and "relying on friends in difficulty" (factor loading: 0.76–0.82). The Cronbach’s α coefficients were 0.883 and 0.815, respectively. 4.4 Correlation Analysis Results Pearson correlation analysis showed that work stress (r = 0.604, p < 0.001) and economic pressure (r = 0.428, p < 0.001) were positively correlated with depression, anxiety (work stress: r = 0.654; economic pressure: r = 0.396), stress (work stress: r = 0.855; economic pressure: r = 0.412), and insomnia (work stress: r = 0.689; economic pressure: r = 0.456). Social support (family, friend, significant other) was negatively correlated with all mental health outcomes (r=-0.437~-0.551, p < 0.001). Quality of life was negatively correlated with mental health problems (r=-0.50~-0.60, p < 0.001) and positively correlated with social support (r = 0.644, p < 0.001). Detailed correlation coefficients are shown in Table 3 and Fig. 2 . Table 3 Correlation Matrix of Key Variables Variables Depression Anxiety Stress Insomnia Social Support Economic Pressure Quality of Life Work stress 0.604** 0.654** 0.855** 0.689** -0.501** 0.387** -0.550** Family support -0.502** -0.551** -0.501** -0.453** 1.000** -0.362** 0.620** Friend support -0.489** -0.538** -0.498** -0.437** 0.892** -0.345** 0.605** Economic Pressure 0.428** 0.396** 0.412** 0.456** -0.358** 1.000** -0.482** Insomnia 0.654** 0.654** 0.689** 1.000** -0.450** 0.456** -0.500** Quality of Life -0.600** -0.550** -0.500** -0.500** 0.644** -0.482** 1.000** *Note: *p < 0.01 (two-tailed) 4.5 Multiple Regression Analysis Results Taking insomnia as the dependent variable, multiple linear regression analysis was conducted with socio-demographic, work-related, and economic factors as independent variables. The results showed that weekly working hours (β = 0.193, p = 0.007), monthly on-call frequency (β = 0.126, p = 0.015), and monthly consumption (β = 0.143, p = 0.003) were significant positive predictors, explaining 9.6% of the variance (F = 4.813, p < 0.001). When adding mental health and social support factors to the model, stress scale score (β = 0.310, p < 0.001) and anxiety scale score (β = 0.164, p = 0.014) were significant positive predictors, while physical domain score of quality of life (β=-0.101, p = 0.049) was a negative predictor, explaining 52.7% of the variance (F = 51.120, p < 0.001). Detailed regression results are shown in Table 4 . Table 4 Multiple Regression Analysis of Factors Influencing Insomnia (n = 469) Predictors Unstandardized β SE Standardized β t p (Constant) -1.632 1.660 - -0.983 0.326 Weekly working hours 0.065 0.024 0.193 2.698 0.007 Monthly on - call frequency 0.934 0.384 0.126 2.434 0.015 Monthly consumption 0.885 0.300 0.143 2.951 0.003 Family support -0.020 0.054 -0.018 -0.378 0.706 Friend support -0.010 0.068 -0.008 -0.142 0.887 Gender \(\:\text{Male=1}\) 0.369 0.460 0.037 0.801 0.423 Age -0.021 0.025 -0.038 -0.829 0.407 Note R² = 0.096; F = 4.813; p < 0.001 4.6 Interaction Effect Results The interaction term of work stress and social support on depression was significant (β=-0.089, p = 0.032). Simple slope analysis showed that in the high social support group (+ 1SD), the positive association between work stress and depression was weaker (slope = 0.321, p < 0.001) than in the low social support group (-1SD) (slope = 0.517, p < 0.001). This indicates that social support can buffer the negative impact of work stress on depression. 4.7 Work Stress × Social Support on Sleep Quality (Scatter Plot Analysis) Grouped by social support level (mean ± SD), correlation analysis showed: Low social support group: Work stress was strongly positively correlated with insomnia (r = 0.58, p < 0.001). Medium social support group: Moderate positive correlation (r = 0.36, p < 0.001). High social support group: Weak positive correlation (r = 0.21, p = 0.003). The scatter plot (Fig. 3 ) intuitively presents this moderating effect: with the increase of social support level, the correlation between work stress and sleep quality gradually weakens, and the slope of the regression line decreases sequentially (β = 0.62 for low support group, β = 0.38 for medium support group, β = 0.23 for high support group), confirming the buffering role of social support. 4.8 Structural Equation Model Results The SEM model included latent variables: work stress (observed indicators: weekly working hours, on-call frequency, stress perception), social support (emotional support, practical support), mental health (emotional depression, anxiety-somatic symptoms, sleep disturbance), and quality of life (physical, psychological, social, environmental domains). The model fit well (χ²/df = 2.371, RMSEA = 0.054, CFI = 0.928, TLI = 0.917, SRMR = 0.048) (Supplementary Table 2). The standardized path coefficients were: work stress→mental health (β = 0.412, p < 0.001), social support→mental health (β=-0.326, p < 0.001), mental health→quality of life (β = 0.684, p < 0.001), confirming all research hypotheses. Detailed information on the path relationships and standardized coefficients among variables is shown in Fig. 4 . 5. Discussion 5.1 Main Findings This study comprehensively analyzed the mental health status and influencing factors of 469 Chinese medical residents, with key findings: The prevalence of moderate-to-severe anxiety (15.8%) and depression (11.9%) among medical residents is prominent, and 6.1% have clinical insomnia, indicating severe mental health challenges. Factor analysis revealed three mental health dimensions (emotional depression, anxiety-somatic symptoms, sleep disturbance) and two social support dimensions (emotional support, practical support), which are reliable structural constructs. Work-related factors (weekly working hours, monthly on-call frequency) and economic factors (monthly consumption) are independent risk factors for mental health problems. Social support plays a significant buffering role in the relationship between work stress and mental health. The SEM confirms the causal paths: work stress negatively affects mental health, social support positively protects mental health, and mental health further affects quality of life. 5.2 Results Interpretation and Comparison 5.2.1 Mental Health Status: Global Commonality and Chinese Characteristics The prevalence of moderate-to-severe depression and anxiety in this study is consistent with the global average (15%-30%) [ 1 , 19 ], indicating that mental health problems among medical residents are a global challenge. However, the unique characteristics of Chinese medical residents are also evident: 83.89% have a monthly income 3000 USD) [ 6 , 20 ], and 92.8% have night shifts (24.9% >7 times/month) [ 6 , 21 ], forming a "low income + high workload" dual pressure pattern. This is different from Western studies that focus more on medical disputes and career burnout [ 7 ], reflecting the stage characteristics of China’s standardized training system [ 22 ]. Viseu et al. (2018) also found that economic pressure was a more prominent stressor for Chinese residents compared to their Western counterparts [ 11 ]. 5.2.2 Weekly Working Hours and Monthly On-call Frequency as Risk Factors Weekly working hours and monthly on-call frequency are significant predictors of insomnia and anxiety, which is consistent with Obeng Nkrumah et al.’s (2025) global scoping review [ 19 ], confirming that excessive workload is a universal risk factor. The positive association between monthly consumption and mental health problems indicates that economic pressure exacerbates negative emotions. This is supported by Viseu et al. (2018), who found that economic stress factors were positively correlated with stress, anxiety and depression among medical staff [ 11 ]. In China, the contradiction between low income and basic consumption needs of medical residents is particularly prominent: the Report on the Current Situation and Salary Consumption of Standardized Training for Resident Physicians in China (2023) reported that 60.04% of residents have monthly consumption of 1000–1999 RMB, while 83.89% earn < 2000 RMB, making economic pressure an important hidden factor affecting mental health [ 6 , 21 ]. 5.2.3 The Buffering Effect of Social Support: Cross-cultural Verification and Dimensional Differences This study confirms the buffering effect of social support, consistent with Cohen’s (2007) Stress-Buffering Model [ 13 , 9 ]. Emotional support and practical support both have protective effects, but family support and friend support have similar weights (β=-0.062 vs. β=-0.058). Notably, "supervisor/colleague support" did not enter the significant path, which differs from Western studies where colleague support is more prominent [ 7 ]. This may be due to the imperfect workplace support system for Chinese medical residents, where the hierarchical clinical culture makes it difficult to form effective peer support networks [ 9 ]. Lio et al. (2016) further pointed out that only 35.32% of Chinese residents hold a positive attitude towards their training departments, which may weaken the protective effect of workplace support [ 22 ]. 5.3 Limitations This study has several limitations: Single-center sampling may limit generalizability; multi-center studies with diverse regions and hospital levels are needed to enhance external validity. Cross-sectional design cannot establish causal relationships; longitudinal studies should track mental health changes over training to clarify temporal associations. Self-reported data may have response bias (e.g., underreporting of severe symptoms); this study reduced bias through anonymous surveys, and future studies can add objective indicators such as physiological measurements (e.g., cortisol levels). The study did not explore the differences in mental health status among different departments (e.g., emergency medicine vs. internal medicine), which may be a direction for further analysis. 5.4 Practical Implications and Suggestions 5.4.1 Mental Health Intervention Strategies Reduce Work Burden: Hospitals should optimize work schedules, limit weekly working hours to ≤ 48 hours (in line with the National Health Commission’s Standardized Training for Residents Management Measures (2023 Edition) [ 22 ]), and adopt a reasonable on-call rotation system (e.g., no more than 3 night shifts per week) to reduce unnecessary night shifts, thereby improving sleep quality [ 7 ]. Strengthen Economic Support: Relevant departments should increase training subsidies, adjust monthly income to match the cost of living (e.g., linking subsidies to local consumption levels), and provide economic assistance for low-income residents to alleviate economic pressure [ 6 ]. Improve Social Support Systems: Establish multi-dimensional support networks: family support (encourage regular communication between residents and their families), peer support (set up resident support groups for experience sharing and emotional mutual assistance), and supervisor support (train supervisors to identify early mental health signals and provide timely guidance) [ 9 , 5 ]. 5.4.2 Sleep Health Targeted Interventions Optimize Sleep Environment: Hospitals should equip on-call lounges with blackout curtains, earplugs, and comfortable beds to improve sleep conditions during shifts. Popularize Sleep Health Education: Include sleep hygiene knowledge (e.g., avoiding electronic devices before bedtime, using relaxation techniques) in standardized training courses to help residents improve sleep self-management capabilities [ 10 , 8 ]. Targeted Support for High-risk Groups: For residents with high work stress and low social support, carry out one-on-one psychological counseling and social support connection services to reduce the risk of sleep disorders [ 9 , 12 ]. 5.4.3 Policy and System-level Support Align resident training subsidies with local minimum wage standards: According to the China Standardized Training for Residents Development Report (2022) , the current average monthly subsidy for residents is approximately 2400 RMB, which should be adjusted based on regional cost of living to reduce economic pressure [ 6 ]. Establish a national occupational mental health monitoring system for medical residents: Regularly assess mental health status, set up early warning mechanisms for high-risk groups (e.g., emergency department residents with frequent night shifts), and incorporate mental health indicators into hospital management evaluations [ 15 , 12 ]. Promote workplace mental health education: Integrate stress management, sleep hygiene, and social support utilization skills into standardized training curricula, with at least 4 hours of specialized training per year [ 9 , 10 ]. Establish a national occupational mental health monitoring system for medical residents: Regularly assess mental health status, set up early warning mechanisms for high-risk groups (e.g., emergency department residents with frequent night shifts), and incorporate mental health indicators into hospital management evaluations [ 15 , 12 , 23 ]. This aligns with the goal of standardized residency training to balance training quality and resident well-being [ 23 ]. 5.5 Future Research Directions Conduct longitudinal studies to track the dynamic changes of mental health during training (e.g., pre-training, mid-training, post-training) and clarify the causal relationships between factors [ 19 , 12 ]. Use mixed-methods research (quantitative + qualitative) to explore the subjective experience of residents with severe mental health problems and identify potential hidden stressors (e.g., interpersonal conflicts, career confusion) [ 4 , 9 ]. Evaluate the effectiveness of targeted interventions (e.g., social support groups, cognitive-behavioral therapy, mindfulness training) through randomized controlled trials to provide evidence-based basis for mental health promotion [ 12 , 19 ]. Explore the differences in mental health status and influencing factors among different departments to develop personalized intervention strategies [ 5 , 21 ]. 6. Conclusion Medical residents in China face significant mental health challenges, with moderate-to-severe anxiety and depression being prominent. Work burden, economic pressure, and lack of social support are key influencing factors, and social support can buffer the negative impact of work stress. The structural equation model confirms the chain relationship: work stress and social support affect mental health, which in turn influences quality of life. The scatter plot further verifies that social support can weaken the negative impact of work stress on sleep quality. Comprehensive strategies involving hospitals, educational institutions, and policymakers are needed to reduce work burden, improve economic support, and strengthen social support systems, thereby promoting the mental health and well-being of medical residents and ensuring the quality of clinical medical services. These findings provide actionable empirical evidence for optimizing China’s resident standardized training system. Abbreviations DASS-21 Depression Anxiety Stress Scale-21 ISI Insomnia Severity Index MSPSS Multidimensional Scale of Perceived Social Support WHOQOL-BREF World Health Organization Quality of Life-BREF SEM Structural Equation Modeling Declarations Ethics approval and consent to participate Ethical approval was obtained from the Institutional Review Board (IRB) of The Second Hospital of Shanxi Medical University on January 15, 2024 (Approval No.: 2024-012). All procedures involving human participants were in accordance with the Declaration of Helsinki. All participants were informed of the study purpose, data usage, and privacy protection measures prior to questionnaire completion. Since the survey was conducted anonymously via the Wenjuanxing platform (no personal identifiable information was collected), informed consent was obtained in the form of implied consent—participants’ voluntary completion and submission of the questionnaire were deemed as consent to participate. Consent for publication Not applicable. No individual participant data or identifiable information is presented in the manuscript. Availability of data and materials The de-identified raw survey data (Supplementary File 2), complete R code for statistical analysis/visualization (Supplementary File 1), and English version of the self-designed questionnaire (Supplementary File 3) are available upon manuscript publication via the BMC Public Health online repository (https://bmcpublichealth.biomedcentral.com/). Prior to publication, the data are stored securely in a password-protected server of The Second Hospital of Shanxi Medical University, complying with the ethical guidelines for participant privacy protection (Ethical Approval No.: 2024-012 issued by the Ethics Committee of The Second Hospital of Shanxi Medical University). Competing interests The authors declare no competing interests (financial or non-financial) related to the submitted work. Funding This study received no specific funding from public, commercial, or non-for-profit sectors. Authors' contributions X.Z. (Xingtao Zhao): Study design, data collection, statistical analysis, and draft manuscript writing; X.W. (Xin Wang): Literature review, data cleaning, and manuscript revision; X.R. (Xiajin Ren)*: Conceptualization, supervision, critical revision of the manuscript for important intellectual content, and final approval of the published version. All authors read and approved the final manuscript. References Reynolds CF 3rd, Clayton PJ, Commentary. Out of the silence: confronting depression in medical students and residents. Acad Med. 2009;84(2):159–60. 10.1097/ACM.0b013e31819397c7 . PMID:19174657. Nair M, Moss N, Bashir A, Garate D, Thomas D, Fu S, Phu D, Pham C. Mental health trends among medical students. Proc (Bayl Univ Med Cent). 2023;36(3):408–10. PMID:37091765; PMCID:PMC10120543. Kwok C. Depression, Stress, and Perceived Medical Errors in Singapore Psychiatry Residents. Acad Psychiatry. 2021;45(2):169–73. 10.1007/s40596-020-01376-w . Epub 2021 Jan 7. PMID:33409942. 杨英 李烨. 汪偌宁, 等。基于满意度视角的住院医师规范化培训现状调查分析 [J]. 中华医学教育杂志, 2024, 44 (2):131–135. 10.3760/cma.j.cn115259-20230824-00167 Fatima S, Soria S, Esteban-Cruciani N. Medical errors during training: how do residents cope? a descriptive study. BMC Med Educ. 2021;21(1):408. 10.1186/s12909-021-02850-1 . PMID:34325691; PMCID:PMC8320044. An J, Chang Y, Zhang X, Zhang M, Lei X, Yang M, Hu Y. Status quo and influencing factors of job burnout among residents in standardized training. Front Public Health. 2024;12:1470739. 10.3389/fpubh.2024.1470739 . PMID:39737464; PMCID:PMC11683051. Shanafelt TD, Boone S, Tan L, Dyrbye LN, Sotile W, Satele D, West CP, Sloan J, Oreskovich MR. Burnout and satisfaction with work-life balance among US physicians relative to the general US population. Arch Intern Med. 2012;172(18):1377–1385. 10.1001/archinternmed.2012.3199 . PMID:22911330. Gong Y, Han T, Chen W, Dib HH, Yang G, Zhuang R, Chen Y, Tong X, Yin X, Lu Z. Prevalence of anxiety and depressive symptoms and related risk factors among physicians in China: a cross-sectional study. PLoS ONE. 2014;9(7):e103242. 10.1371/journal.pone.0103242 . PMID:25050618; PMCID:PMC4106870. Fu C, Wang G, Shi X, Cao F. Social support and depressive symptoms among physicians in tertiary hospitals in China: a cross-sectional study. BMC Psychiatry. 2021;21(1):217. 10.1186/s12888-021-03219-w . PMID:33926402; PMCID:PMC8082214. Qin A, Hu F, Qin W, Dong Y, Li M, Xu L. Educational degree differences in the association between work stress and depression among Chinese healthcare workers: Job satisfaction and sleep quality as the mediators. Front Public Health. 2023;11:1138380. 10.3389/fpubh.2023.1138380 . PMID:37064682; PMCID:PMC10120543. Viseu J, Leal R, de Jesus SN, Pinto P, Pechorro P, Greenglass E. Relationship between economic stress factors and stress, anxiety, and depression: Moderating role of social support. Psychiatry Res. 2018;268:102–7. Epub 2018 Jul 7. PMID:30015107. He Y, Lin S, Wang Y, Zhang B, Wang Y, Sheng S, Gu X, Wang W. A survey on mental health among resident physicians: psychological resilience as a mediator. BMC Psychiatry. 2025;21(1):87. 10.1186/s12888-025-06517-9 . PMID:39891091; PMCID:PMC11786329. Cohen S, Janicki-Deverts D, Miller GE. Psychological Stress and Disease. JAMA. 2007;298(14):1685–7. 10.1001/jama.298.14.1685 . Lovibond PF, Lovibond SH. The structure of negative emotional states: comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. Behav Res Ther. 1995;33(3):335 – 43. 10.1016/0005-7967(94)00075-u . PMID: 7726811. Morin CM, Belleville G, Bélanger L, Ivers H. The Insomnia Severity Index: psychometric indicators to detect insomnia cases and evaluate treatment response. Sleep. 2011;34(5):601–8. 10.1093/sleep/34.5.601 . PMID: 21532953; PMCID: PMC3079939. Zimet GD, Powell SS, Farley GK, Werkman S, Berkoff KA. Psychometric characteristics of the Multidimensional Scale of Perceived Social Support. J Pers Assess. 1990 Winter;55(3–4):610-7. 10.1080/00223891.1990.9674095 . PMID: 2280326. Zhao X, Wang X, Ren X. Supplementary File 3: English version of the self-designed socio-demographic and work-related questionnaire. BMC Public Health. [Submitted alongside the manuscript]. The World Health Organization Quality of Life Assessment (WHOQOL): development and general psychometric properties. Soc Sci Med. 1998;46(12):1569-85. 10.1016/s0277-9536(98)00009-4 . PMID: 9672396. Obeng Nkrumah S, Adu MK, Agyapong B, da Luz Dias R, Agyapong VIO. Prevalence and correlates of depression, anxiety, and burnout among physicians and postgraduate medical trainees: a scoping review of recent literature. Front Public Health. 2025;13:1537108. 10.3389/fpubh.2025.1537108 . PMID:40697832; PMCID:PMC12279716. Getz M. Education and earnings in the health professions. J Vet Med Educ. 2012 Fall;39(3):247–256. 10.3138/jvme.0512.036 . PMID:22951459. Wang H, He J, Zhang D, Wu Y, Wang P, Cai H. Investigation and analysis of standardized training for residents of general practitioners of Gansu Province in China. BMC Fam Pract. 2020;21(1):112. 10.1186/s12875-020-01185-y . PMID:32560693; PMCID:PMC7304171. Lio J, Dong H, Ye Y, Cooper B, Reddy S, Sherer R. Standardized residency programs in China: perspectives on training quality. Int J Med Educ. 2016;7:220–221. 10.5116/ijme.5780.9b85 . PMID:27421072; PMCID:PMC4958345. Manage. 2020;35(2):592–605. doi:10.1002/hpm.2970. Epub 2019 Nov 19. PMID:31742772. He Y, Qian W, Shi L, Zhang K, Huang J. Standardized residency training: An equalizer for residents at different hospitals in Shanghai. China? Int J Health Plann Manage. 2020;35(2):592–605. 10.1002/hpm.2970 . Epub 2019 Nov 19. PMID:31742772. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFile1RCodeforDataAnalysisandVisualizationCorrected.docx SupplementaryFile2DeidentifiedRawData.dsv.xlsx SupplementaryFile3EnglishVersionoftheSelfDesignedQuestionnaireCorrected.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 25 Feb, 2026 Editor assigned by journal 23 Feb, 2026 Editor invited by journal 29 Jan, 2026 Submission checks completed at journal 27 Jan, 2026 First submitted to journal 27 Jan, 2026 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8598823","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":596951573,"identity":"a44b1e67-f899-4c99-87ce-f0a060d3d1b3","order_by":0,"name":"Xingtao Zhao","email":"","orcid":"","institution":"The Second Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xingtao","middleName":"","lastName":"Zhao","suffix":""},{"id":596951574,"identity":"23f60573-e7d7-466e-a74f-35909d048cf8","order_by":1,"name":"Xin Wang","email":"","orcid":"","institution":"The Second Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Wang","suffix":""},{"id":596951575,"identity":"21fd2870-8095-49da-9f00-de919c5f47a9","order_by":2,"name":"Xiajin Ren","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYLCCBAY2Hgb2xsYHH0jTwnO42XAGaVZJpLdJcxCj0Ly995jEgxo+GXPJhw3SDAx2croNBLTInDmXbJBwjI3HcnZig3EBQ7Kx2QFCzpHIMXyQwMbGY3A7sSF5BsOBxG0Etci/MTiQ8A+o5ebBhsM8RGmR4DF8kNgG1HKDsbGZOC08OcYGiX1ALWcSmxlnGBDjF/YzZpI/vh2zNzh+/PmPDxV2cgS1QMExKG1AnHIQqCFe6SgYBaNgFIw8AACG4T7V/1cVggAAAABJRU5ErkJggg==","orcid":"","institution":"The Second Hospital of Shanxi Medical University","correspondingAuthor":true,"prefix":"","firstName":"Xiajin","middleName":"","lastName":"Ren","suffix":""}],"badges":[],"createdAt":"2026-01-14 07:38:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8598823/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8598823/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103733793,"identity":"a48c1208-9364-4855-a70a-1f4536bdbb7a","added_by":"auto","created_at":"2026-03-02 09:29:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":110746,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of mental health status (depression, anxiety, stress, insomnia) among 469 Chinese medical residents (stacked bar chart). Note: \"Mild\" includes mild and no symptoms; \"Moderate-to-severe\" includes moderate and severe symptoms for depression/anxiety/stress, and clinical insomnia for ISI.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8598823/v1/aa1e81b726bc46c9bc1391ab.png"},{"id":103733818,"identity":"4748b9ec-bedc-4603-88db-7515ea58a9c9","added_by":"auto","created_at":"2026-03-02 09:29:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":105786,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eCorrelation matrix heatmap of key variables (work stress, economic pressure, social support, mental health, quality of life). Note: Red indicates positive correlation, blue indicates negative correlation; darker color indicates stronger correlation; all correlations are significant at p\u0026lt;0.001.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8598823/v1/7690f459698c6ff911550152.png"},{"id":103733784,"identity":"0130aa4f-c207-4bfa-b493-4025590a6e84","added_by":"auto","created_at":"2026-03-02 09:29:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":155851,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eInteraction effect of work stress and social support on insomnia (scatter plot with regression lines). Note: The x-axis represents work stress score (0-42), and the y-axis represents insomnia severity index (ISI, 0-28). Red = low social support (-1 SD), green = medium social support (mean), blue = high social support (+1 SD). Shaded areas represent 95% confidence intervals.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8598823/v1/dcaa7ea8c23c79230cf6c9cc.png"},{"id":103733792,"identity":"2b06f0cf-9c3a-40cd-8867-143d4d4f1555","added_by":"auto","created_at":"2026-03-02 09:29:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":61969,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eStructural Equation Model of Key Variables. Note: Rectangles represent observed variables, rounded rectangles represent latent variables, solid lines represent significant paths (***p\u0026lt;0.001), and standardized coefficients are labeled above the paths.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8598823/v1/a72e956be3c8b6754e75d976.png"},{"id":104400379,"identity":"9336de4d-c1b2-4f76-82be-e2b7825c5c2c","added_by":"auto","created_at":"2026-03-11 12:09:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1850749,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8598823/v1/4a3dc7bd-9afa-4db3-a743-b23c7aa694bb.pdf"},{"id":103733819,"identity":"9057f429-aae7-4c8b-bd97-822e30ac8a37","added_by":"auto","created_at":"2026-03-02 09:29:43","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":18460,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile1RCodeforDataAnalysisandVisualizationCorrected.docx","url":"https://assets-eu.researchsquare.com/files/rs-8598823/v1/cb75d14bb81b3d9363cb37c7.docx"},{"id":103733814,"identity":"4d82f2db-52e3-4ee0-90fa-b210236506ff","added_by":"auto","created_at":"2026-03-02 09:29:42","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":239861,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile2DeidentifiedRawData.dsv.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8598823/v1/839945115bcd0bbfc92f9a2f.xlsx"},{"id":103733782,"identity":"4ee5e439-fe67-4de0-a3f4-0ab85689ba7b","added_by":"auto","created_at":"2026-03-02 09:29:31","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":14193,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile3EnglishVersionoftheSelfDesignedQuestionnaireCorrected.docx","url":"https://assets-eu.researchsquare.com/files/rs-8598823/v1/9961e623ea48e72b9b41a99f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dual Pressure of Low Income and High Workload: Impact on Mental Health and Sleep Quality Among Chinese Medical Residents, and the Buffering Role of Social Support","fulltext":[{"header":"1. Introduction","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Research Background\u003c/h2\u003e \u003cp\u003eMental health crises among medical trainees have become a global public health concern, with the prevalence of depression and anxiety among medical residents being 2\u0026ndash;3 times higher than that of the general population [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This not only threatens the physical and mental well-being of residents but also imposes potential risks on clinical work quality and medical safety [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. China\u0026rsquo;s standardized training system covers over 100,000 residents annually [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], and while global studies have confirmed work overload and irregular schedules as core risk factors [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], the mental health challenges faced by Chinese medical residents exhibit distinct characteristics rooted in this system.\u003c/p\u003e \u003cp\u003eChinese medical residents undergo intensive training characterized by a unique \"low income\u0026thinsp;+\u0026thinsp;high workload\" dual pressure pattern. The Report on the Current Situation and Salary Consumption of Standardized Training for Resident Physicians in China (2023) pointed out that the income level of Chinese standardized training residents is generally low relative to their high-intensity clinical workloads [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Meanwhile, relevant studies have indicated that Chinese residents are exposed to frequent night shifts and prolonged working hours, which further exacerbate their work-related burden [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In contrast, research on medical residents in Europe and America has mainly focused on the impacts of medical disputes and career burnout rather than economic pressure [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This prominent contradiction between low income and heavy work burden has become an understudied hidden stressor in existing literature.\u003c/p\u003e \u003cp\u003eExisting studies on Chinese medical residents\u0026rsquo; mental health have mainly focused on single-factor associations (e.g., work stress \u0026rarr; depression) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], lacking systematic exploration of the multi-dimensional structure of mental health and the interactive mechanisms between factors. Three key research gaps remain: first, few studies have distinguished the differential buffering effects of emotional support and practical support, and workplace support (from colleagues/supervisors) has shown limited protective effects in the Chinese hierarchical clinical culture [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]; second, the chain effect of sleep quality between dual pressure and mental health is not fully clarified [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]; third, the interactive role of economic pressure with work stress and social support has not been adequately verified [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This study focuses on the mediating role of sleep quality and the interaction effect of economic pressure, addressing the above gaps.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Research Questions \u0026amp; Hypotheses\u003c/h2\u003e \u003cp\u003eResearch Questions\u003c/p\u003e \u003cp\u003eWhat is the overall mental health status of Chinese medical residents, including the prevalence of depression, anxiety, stress, and clinical insomnia under the dual pressure pattern?\u003c/p\u003e \u003cp\u003eWhat are the key influencing factors of mental health (work-related, economic, social support, etc.) among this group?\u003c/p\u003e \u003cp\u003eDo emotional support and practical support exert differential buffering effects on the relationship between dual pressure and mental health?\u003c/p\u003e \u003cp\u003eWhat is the structural relationship between dual pressure, social support, mental health, and quality of life?\u003c/p\u003e \u003cp\u003eResearch Hypotheses\u003c/p\u003e \u003cp\u003eH1: Higher dual pressure (workload\u0026thinsp;+\u0026thinsp;economic pressure) is positively associated with depression, anxiety, and insomnia.\u003c/p\u003e \u003cp\u003eH2: Higher social support (emotional and practical) is negatively associated with mental health problems.\u003c/p\u003e \u003cp\u003eH3: Social support (emotional and practical) moderates the association between dual pressure and mental health.\u003c/p\u003e \u003cp\u003eH4: Mental health plays a mediating role between dual pressure/social support and quality of life.\u003c/p\u003e \u003cp\u003eThis study aims to fill the above gaps by integrating multiple statistical methods (factor analysis, interaction effect tests, structural equation modeling) to comprehensively analyze the mental health status and influencing mechanisms of Chinese medical residents. The findings are expected to provide targeted empirical evidence for optimizing the standardized training system and promoting occupational mental health in the medical field.\u003c/p\u003e \u003c/div\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Mental Health Status of Medical Residents\u003c/h2\u003e \u003cp\u003eGlobal studies have consistently shown that medical residents are a high-risk group for mental health issues. Multiple studies have confirmed that the mental health risk of medical residents is significantly higher than that of the general population [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In China, relevant studies have reported similar results: He et al. (2025) conducted a cross-sectional survey of 453 standardized training residents in Chinese tertiary hospitals, revealing that sleep disturbances (SD) exert negative impacts on residents\u0026rsquo; mental health, with psychological resilience (PR) partially mediating the association between SD and suicidal ideation (SI), as well as between SD and life satisfaction (LS) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Qin et al. (2023) further indicated that healthcare workers facing heavier workloads are more prone to depression, and this association is particularly prominent among residents in high-pressure departments such as internal medicine and emergency medicine [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The high incidence of mental health problems among medical residents is closely linked to their unique training environment, including excessive clinical workload, high risk of medical errors, and stringent performance assessment requirements [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Specifically, An et al. (2024) confirmed that heavy clinical tasks and strict training evaluations are key factors contributing to job burnout among standardized training residents [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], while Fatima et al. (2021) noted that the pressure of avoiding medical errors during training further exacerbates residents\u0026rsquo; psychological burden [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Work Stress and Mental Health\u003c/h2\u003e \u003cp\u003eWork stress is a core factor affecting the mental health of medical residents. The main stressors include heavy clinical workload, high pressure of avoiding medical errors, and stringent training evaluations [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Shanafelt et al. (2012) revealed that physicians with poor work-life balance showed significantly higher levels of emotional exhaustion, a core dimension of burnout, compared with those with balanced work and life [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. For resident physicians, night shifts are a key contributor to sleep disturbances, and accumulating evidence has confirmed that sleep disorders can further mediate the negative impact of work stress on mental health [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In China, the work stress of standardized training residents is also not optimistic: An et al. (2024) identified heavy clinical tasks and strict training assessments as key factors leading to job burnout among Chinese residents [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Meanwhile, Qin et al. (2023) found that heavier workloads were positively associated with higher depressive symptoms among Chinese healthcare workers, suggesting that overwork is an important driver of mental health problems in this population [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Additionally, Gong et al. (2014) noted that high work risk and long working hours were common risk factors for anxiety and depressive symptoms among Chinese physicians [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3 The Impact of Social Support\u003c/h2\u003e \u003cp\u003eSocial support plays a crucial buffering role in the relationship between stress and mental health, as proposed by the Stress-Buffering Model [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Cohen et al. (2007) updated the model, suggesting that emotional support can regulate the hypothalamic-pituitary-adrenal axis function, reducing the physiological response to stress [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. For medical residents, social support from family, friends, and colleagues can alleviate work pressure and negative emotions [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, existing studies often ignore the differences between emotional support and practical support, and the protective effect of workplace support (colleagues, supervisors) is less significant in the Chinese context [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Gong et al. (2014) attributed this to the high-pressure clinical environment and strict hierarchical relationships in Chinese hospitals, which hinder the formation of effective peer support networks among medical staff [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Income Level and Mental Health\u003c/h2\u003e \u003cp\u003eEconomic pressure is an important hidden factor affecting the mental health of medical residents. Viseu et al. (2018) confirmed that economic stress factors were positively correlated with stress, anxiety and depression symptoms among medical staff, and social support could moderate this adverse association [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. For Chinese standardized training residents, this issue is particularly prominent: the \u003cem\u003eReport on the Current Situation and Salary Consumption of Standardized Training for Resident Physicians in China (2023)\u003c/em\u003e pointed out that their income level is generally low relative to high-intensity clinical workloads, which directly induces economic pressure and further exacerbates psychological burden [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This is consistent with the conclusion of Viseu et al. (2018), indicating that economic stress is a universal risk factor for mental health problems among medical practitioners across different contexts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Summary and Research Gaps\u003c/h2\u003e \u003cp\u003eExisting studies have confirmed the high incidence of mental health problems (e.g., depression, anxiety, and burnout) among medical residents, with work stress, social support, and economic factors identified as key influencing variables [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, several research gaps remain to be addressed: 1) Lack of systematic analysis of the mental health status, particularly failing to reveal the potential dimensional structure and comorbidity patterns of depression, anxiety, and insomnia among this population [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]; 2) Insufficient exploration of the interaction mechanisms between multiple factors, especially the differential buffering effects of different types of social support (e.g., emotional vs. practical support) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]; 3) Few studies have adopted structural equation modeling to clarify the chain mediating or moderating relationships between work stress, social support, mental health outcomes, and quality of life [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. To fill these gaps, this study integrates multiple statistical methods to comprehensively explore the mental health status and influencing factors of Chinese medical residents, aiming to provide empirical evidence for targeted intervention strategies.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Study Design \u0026amp; Participants\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDesign\u003c/b\u003e: Cross-sectional quantitative study.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eParticipants\u003c/b\u003e: Convenience sampling was used to recruit residents from a tertiary hospital in China from January to April 2024.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eInclusion criteria\u003c/b\u003e: \u0026ge;18 years old, enrolled in standardized training for more than 3 months.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eExclusion criteria\u003c/b\u003e: History of severe mental illness (diagnosed by a psychiatrist), incomplete questionnaire responses.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSample Size\u003c/b\u003e: A total of 500 questionnaires were distributed, 453 were initially valid, and 16 additional valid responses were included after further data cleaning (missing key items\u0026thinsp;\u0026le;\u0026thinsp;10%), resulting in a final sample of 469 (response rate 93.8%). Sample size was calculated based on a previous study reporting a 20% prevalence of anxiety among medical residents [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Setting α\u0026thinsp;=\u0026thinsp;0.05 and power\u0026thinsp;=\u0026thinsp;0.8, the minimum sample size was estimated to be 385 using the formula n\u0026thinsp;=\u0026thinsp;Z\u0026sup2;π(1-π)/d\u0026sup2;. This study included 469 valid responses, meeting the statistical requirement. The sample included 277 females (59.1%) and 192 males (40.9%), aged 22\u0026ndash;40 years (mean 25.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3 years). Among them, 80.35% were professional master's degree residents, 50.99% were in the second year of training, and the department distribution was: internal medicine (28.26%), surgery (8.83%), emergency medicine (3.31%), orthopedics (11.26%), obstetrics \u0026amp; gynecology (7.28%), anesthesiology (7.06%), neurology (4.42%), radiology (4.19%), and other departments (33.07%) (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for detailed distribution).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Measures\u003c/h2\u003e \u003cp\u003eThe socio-demographic and work-related questionnaire used in this study was developed specifically for investigating the dual pressure of low income and high workload among Chinese medical residents. It was not adapted from any previously published questionnaires, as no existing tools fully align with the research focus on residency training-related stressors in the Chinese context. The questionnaire consists of two parts: Part A includes socio-demographic and professional information, and Part B covers work-related factors and subjective perceptions. All items were designed following standardized questionnaire development principles, including specific language framing, mutually exclusive response categories, and clear skip patterns. The English version of the questionnaire is available as Supplementary File 3.\u003c/p\u003e \u003cp\u003eAssessment Tools Information Table\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTools\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDimensions \u0026amp; Scoring\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReliability\u003c/p\u003e \u003cp\u003e(Cronbach\u0026rsquo;s α)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMental Health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDASS\u0026thinsp;\u0026minus;\u0026thinsp;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 dimensions (depression/anxiety/stress), 7 items each, 4-point scale (0\u0026thinsp;=\u0026thinsp;never to 3\u0026thinsp;=\u0026thinsp;always)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] Lovibond SH, Lovibond PF. The structure of negative emotional states: Comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. Behav Res Ther. 1995;33(3):335\u0026ndash;343. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/0005-7967(94)00075-L\u003c/span\u003e\u003cspan address=\"10.1016/0005-7967(94)00075-L\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMental Health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eISI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 items, 5-point scale (0\u0026thinsp;=\u0026thinsp;none to 4\u0026thinsp;=\u0026thinsp;very severe); \u0026ge;15\u0026thinsp;=\u0026thinsp;clinical insomnia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]Bastien CH, Valli\u0026egrave;res A, Morin CM. Insomnia Severity Index (ISI): Psychometric properties in primary insomnia and secondary insomnia. Sleep Med. 2001;2(4):297\u0026ndash;307. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S1389-9457(01)00062-4\u003c/span\u003e\u003cspan address=\"10.1016/S1389-9457(01)00062-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial Support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMSPSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 dimensions (family/friend/significant other support), 4 items each, 7-point scale [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]Zimet GD, Dahlem NW, Zimet SG, Farley GD. The Multidimensional Scale of Perceived Social Support. J Pers Assess. 1988;52(1):30\u0026ndash;41. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/00223891.1988.9924518\u003c/span\u003e\u003cspan address=\"10.1080/00223891.1988.9924518\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWork-related Factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelf-designed questionnaire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeekly working hours, monthly on-call frequency, self-reported work stress (low/medium/high) .\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]See Supplementary File 3 for the English version.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocio-economic Factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelf-designed questionnaire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGender, age, education, monthly income, monthly consumption, marital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]See Supplementary File 3 for the English version.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuality of Life\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWHOQOL - BREF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 dimensions (physical/psychological/social/environmental), 26 items, 5-point scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]WHO. The World Health Organization Quality of Life Assessment (WHOQOL): Development and general psychometric properties. Soc Sci Med. 1998;46(12):1569\u0026ndash;1585. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0277-9536(98)00085-6\u003c/span\u003e\u003cspan address=\"10.1016/S0277-9536(98)00085-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe self-designed questionnaires for work-related and socio-economic factors were developed specifically for this study to adapt to the characteristics of Chinese medical residents. The English version is provided as Supplementary File 3, and the Chinese version (used for data collection) is available upon reasonable request from the corresponding author.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Data Collection\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003e was obtained from the Ethics Committee of The Second Hospital of Shanxi Medical University on January 15, 2024 (Approval No.: 2024-012). The questionnaire was distributed online via the Wenjuanxing platform, and participants completed it anonymously after reading the informed consent form. The survey took approximately 15 minutes to complete, and data were exported and cleaned to eliminate invalid responses (missing key items\u0026thinsp;\u0026gt;\u0026thinsp;10%).\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Statistical Analysis\u003c/h2\u003e \u003cp\u003eAnalysis Information Table\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnalysis Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePurpose\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSoftware/Tools\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDescriptive Statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescribe sample characteristics and variable distributions (frequency, mean, SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSPSS 26.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor Analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtract potential factors of mental health/social support (KMO/Bartlett\u0026rsquo;s test)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSPSS 26.0, R (factoextra package)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorrelation Analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnalyze relationships between work stress, social support, and mental health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSPSS 26.0, R (corrplot package)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple Linear Regression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIdentify independent predictors of mental health outcomes (with multicollinearity test: VIF\u0026thinsp;\u0026lt;\u0026thinsp;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSPSS 26.0, R (sjPlot package)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInteraction Effect Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTest moderating role of social support (cross-product terms\u0026thinsp;+\u0026thinsp;simple slope)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR (interactions package)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStructural Equation Model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVerify causal paths between variables (χ\u0026sup2;/df, RMSEA, CFI, TLI, SRMR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAMOS 24.0, R (lavaan package)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 R Code for Data Analysis and Visualization\u003c/h2\u003e \u003cp\u003eAll statistical analyses (factor analysis, correlation analysis, multiple linear regression, interaction effect test, structural equation modeling) and data visualization (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) were performed using R 4.3.0 with packages including ggplot2 3.4.4, lavaan 0.6\u0026ndash;16, factoextra 1.0.7, and interactions 1.1.5; the complete and reproducible R code (with detailed annotations) is provided in Supplementary File 1, which can be directly run to reproduce the results.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Sample Characteristics\u003c/h2\u003e \u003cp\u003eThe sample was predominantly female (59.10%) and aged 25\u0026ndash;26 years (50.30%). Most residents had a bachelor\u0026rsquo;s degree (59.91%), were professional master\u0026rsquo;s degree candidates (80.35%), and were in the second year of training (50.99%). Regarding department distribution: internal medicine (133, 28.26%), surgery (41, 8.83%), orthopedics (53, 11.26%), obstetrics \u0026amp; gynecology (34, 7.28%), anesthesiology (33, 7.06%), emergency medicine (15, 3.31%), neurology (21, 4.42%), radiology (20, 4.19%), and other departments (155, 33.07%). Regarding work-related characteristics, the average weekly working time was 51.75\u0026thinsp;\u0026plusmn;\u0026thinsp;15.04 hours, 92.8% had night shifts, and 65.90% were on-call 3\u0026ndash;5 times per month. Economically, 83.89% had a monthly income of \u0026lt;\u0026thinsp;2000 RMB, and 60.04% had a monthly consumption of 1000\u0026ndash;1999 RMB. Detailed characteristics are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample Characteristics (n\u0026thinsp;=\u0026thinsp;469)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDetails\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026ndash;26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEducational Background\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBachelor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoctor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTraining Grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFirst year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecond year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThird year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eDepartment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInternal Medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrthopedics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObstetrics \u0026amp; Gynecology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnesthesiology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmergency Medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeurology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRadiology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther Departments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMonthly Income (RMB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2000\u0026ndash;3999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekly Working Hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.75\u0026thinsp;\u0026plusmn;\u0026thinsp;15.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMonthly On-call Frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;2 times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u0026ndash;5 times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;5 times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eWork Stress Perception\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: All data are based on 469 valid responses; percentages are rounded to two decimal places.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Mental Health Status\u003c/h2\u003e \u003cp\u003eThe mean scores of DASS-21 dimensions were: depression (5.08\u0026thinsp;\u0026plusmn;\u0026thinsp;7.36), anxiety (5.53\u0026thinsp;\u0026plusmn;\u0026thinsp;6.99), stress (6.63\u0026thinsp;\u0026plusmn;\u0026thinsp;8.06). The prevalence of moderate-to-severe depression, anxiety, and stress was 11.9%, 15.8%, and 3.6%, respectively. The mean ISI score was 5.53\u0026thinsp;\u0026plusmn;\u0026thinsp;5.09, with 25.2% having subclinical insomnia and 6.1% having clinical insomnia (moderate-severe). Detailed severity distributions are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMental Health Status Distribution (n\u0026thinsp;=\u0026thinsp;469)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOutcome Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSeverity Classification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003cp\u003e(n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDASS\u0026thinsp;\u0026minus;\u0026thinsp;21 Depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e5.08\u0026thinsp;\u0026plusmn;\u0026thinsp;7.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e84.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDASS\u0026thinsp;\u0026minus;\u0026thinsp;21 Anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e5.53\u0026thinsp;\u0026plusmn;\u0026thinsp;6.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e77.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDASS\u0026thinsp;\u0026minus;\u0026thinsp;21 Stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e6.63\u0026thinsp;\u0026plusmn;\u0026thinsp;8.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e88.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eISI Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e5.53\u0026thinsp;\u0026plusmn;\u0026thinsp;5.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo clinical significance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e69.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSubclinical insomnia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClinical insomnia (moderate - severe)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Factor Analysis Results\u003c/h2\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e4.3.1 Mental Health Factors (DASS-21\u0026thinsp;+\u0026thinsp;ISI)\u003c/h2\u003e \u003cp\u003eKMO\u0026thinsp;=\u0026thinsp;0.892 (\u0026gt;\u0026thinsp;0.8), Bartlett\u0026rsquo;s sphericity test χ\u0026sup2;=5862.371, df\u0026thinsp;=\u0026thinsp;300, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, indicating suitability for factor analysis. PCA with Varimax rotation extracted 3 factors with eigenvalues\u0026thinsp;\u0026gt;\u0026thinsp;1, cumulative variance explanation rate\u0026thinsp;=\u0026thinsp;69.76%:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eFactor 1 (emotional depression): Included items such as \"feeling depressed\", \"no expectation for the future\", and \"lack of enthusiasm\" (factor loading: 0.78\u0026ndash;0.86).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFactor 2 (anxiety-somatic symptoms): Included \"nervousness\", \"dyspnea\", and \"abnormal heartbeat\" (factor loading: 0.72\u0026ndash;0.81).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFactor 3 (sleep disturbance): Included \"difficulty falling asleep\", \"difficulty maintaining sleep\", and \"early awakening\" (factor loading: 0.75\u0026ndash;0.83).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe Cronbach\u0026rsquo;s α coefficients of the three factors were 0.876, 0.821, and 0.798, respectively, with composite reliability (CR)\u0026thinsp;\u0026gt;\u0026thinsp;0.8 and average variance extracted (AVE)\u0026thinsp;\u0026gt;\u0026thinsp;0.5, indicating good reliability and validity (Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e4.3.2 Social Support Factors (MSPSS)\u003c/h2\u003e \u003cp\u003eKMO\u0026thinsp;=\u0026thinsp;0.865, Bartlett\u0026rsquo;s sphericity test χ\u0026sup2;=3217.542, df\u0026thinsp;=\u0026thinsp;66, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. Two factors were extracted, cumulative variance explanation rate\u0026thinsp;=\u0026thinsp;72.34%:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eFactor 1 (emotional support): Included \"family emotional support\" and \"sharing joys and sorrows with friends\" (factor loading: 0.80\u0026ndash;0.87).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFactor 2 (practical support): Included \"family practical help\" and \"relying on friends in difficulty\" (factor loading: 0.76\u0026ndash;0.82).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe Cronbach\u0026rsquo;s α coefficients were 0.883 and 0.815, respectively.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Correlation Analysis Results\u003c/h2\u003e \u003cp\u003ePearson correlation analysis showed that work stress (r\u0026thinsp;=\u0026thinsp;0.604, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and economic pressure (r\u0026thinsp;=\u0026thinsp;0.428, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were positively correlated with depression, anxiety (work stress: r\u0026thinsp;=\u0026thinsp;0.654; economic pressure: r\u0026thinsp;=\u0026thinsp;0.396), stress (work stress: r\u0026thinsp;=\u0026thinsp;0.855; economic pressure: r\u0026thinsp;=\u0026thinsp;0.412), and insomnia (work stress: r\u0026thinsp;=\u0026thinsp;0.689; economic pressure: r\u0026thinsp;=\u0026thinsp;0.456). Social support (family, friend, significant other) was negatively correlated with all mental health outcomes (r=-0.437~-0.551, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Quality of life was negatively correlated with mental health problems (r=-0.50~-0.60, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and positively correlated with social support (r\u0026thinsp;=\u0026thinsp;0.644, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Detailed correlation coefficients are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation Matrix of Key Variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStress\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInsomnia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSocial Support\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEconomic Pressure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eQuality of Life\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWork stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.604**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.654**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.855**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.689**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.501**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.387**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.550**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.502**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.551**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.501**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.453**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.362**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.620**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFriend support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.489**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.538**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.498**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.437**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.892**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.345**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.605**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomic Pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.428**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.396**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.412**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.456**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.358**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.482**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsomnia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.654**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.654**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.689**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.450**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.456**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.500**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuality of Life\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.600**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.550**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.500**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.500**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.644**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.482**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.000**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e*Note: *p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 (two-tailed)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Multiple Regression Analysis Results\u003c/h2\u003e \u003cp\u003eTaking insomnia as the dependent variable, multiple linear regression analysis was conducted with socio-demographic, work-related, and economic factors as independent variables. The results showed that weekly working hours (β\u0026thinsp;=\u0026thinsp;0.193, p\u0026thinsp;=\u0026thinsp;0.007), monthly on-call frequency (β\u0026thinsp;=\u0026thinsp;0.126, p\u0026thinsp;=\u0026thinsp;0.015), and monthly consumption (β\u0026thinsp;=\u0026thinsp;0.143, p\u0026thinsp;=\u0026thinsp;0.003) were significant positive predictors, explaining 9.6% of the variance (F\u0026thinsp;=\u0026thinsp;4.813, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). When adding mental health and social support factors to the model, stress scale score (β\u0026thinsp;=\u0026thinsp;0.310, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and anxiety scale score (β\u0026thinsp;=\u0026thinsp;0.164, p\u0026thinsp;=\u0026thinsp;0.014) were significant positive predictors, while physical domain score of quality of life (β=-0.101, p\u0026thinsp;=\u0026thinsp;0.049) was a negative predictor, explaining 52.7% of the variance (F\u0026thinsp;=\u0026thinsp;51.120, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Detailed regression results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultiple Regression Analysis of Factors Influencing Insomnia (n\u0026thinsp;=\u0026thinsp;469)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnstandardized β\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandardized β\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Constant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekly working hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonthly on - call frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonthly consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFriend support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{Male=1}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.423\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.407\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNote\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eR\u0026sup2; = 0.096; F\u0026thinsp;=\u0026thinsp;4.813; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Interaction Effect Results\u003c/h2\u003e \u003cp\u003eThe interaction term of work stress and social support on depression was significant (β=-0.089, p\u0026thinsp;=\u0026thinsp;0.032). Simple slope analysis showed that in the high social support group (+\u0026thinsp;1SD), the positive association between work stress and depression was weaker (slope\u0026thinsp;=\u0026thinsp;0.321, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) than in the low social support group (-1SD) (slope\u0026thinsp;=\u0026thinsp;0.517, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This indicates that social support can buffer the negative impact of work stress on depression.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.7 Work Stress \u0026times; Social Support on Sleep Quality (Scatter Plot Analysis)\u003c/h2\u003e \u003cp\u003eGrouped by social support level (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD), correlation analysis showed:\u003c/p\u003e \u003cp\u003eLow social support group: Work stress was strongly positively correlated with insomnia (r\u0026thinsp;=\u0026thinsp;0.58, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eMedium social support group: Moderate positive correlation (r\u0026thinsp;=\u0026thinsp;0.36, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eHigh social support group: Weak positive correlation (r\u0026thinsp;=\u0026thinsp;0.21, p\u0026thinsp;=\u0026thinsp;0.003).\u003c/p\u003e \u003cp\u003eThe scatter plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) intuitively presents this moderating effect: with the increase of social support level, the correlation between work stress and sleep quality gradually weakens, and the slope of the regression line decreases sequentially (β\u0026thinsp;=\u0026thinsp;0.62 for low support group, β\u0026thinsp;=\u0026thinsp;0.38 for medium support group, β\u0026thinsp;=\u0026thinsp;0.23 for high support group), confirming the buffering role of social support.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e4.8 Structural Equation Model Results\u003c/h2\u003e \u003cp\u003eThe SEM model included latent variables: work stress (observed indicators: weekly working hours, on-call frequency, stress perception), social support (emotional support, practical support), mental health (emotional depression, anxiety-somatic symptoms, sleep disturbance), and quality of life (physical, psychological, social, environmental domains). The model fit well (χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;2.371, RMSEA\u0026thinsp;=\u0026thinsp;0.054, CFI\u0026thinsp;=\u0026thinsp;0.928, TLI\u0026thinsp;=\u0026thinsp;0.917, SRMR\u0026thinsp;=\u0026thinsp;0.048) (Supplementary Table\u0026nbsp;2). The standardized path coefficients were: work stress\u0026rarr;mental health (β\u0026thinsp;=\u0026thinsp;0.412, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), social support\u0026rarr;mental health (β=-0.326, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), mental health\u0026rarr;quality of life (β\u0026thinsp;=\u0026thinsp;0.684, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), confirming all research hypotheses. Detailed information on the path relationships and standardized coefficients among variables is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Main Findings\u003c/h2\u003e \u003cp\u003eThis study comprehensively analyzed the mental health status and influencing factors of 469 Chinese medical residents, with key findings:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThe prevalence of moderate-to-severe anxiety (15.8%) and depression (11.9%) among medical residents is prominent, and 6.1% have clinical insomnia, indicating severe mental health challenges.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFactor analysis revealed three mental health dimensions (emotional depression, anxiety-somatic symptoms, sleep disturbance) and two social support dimensions (emotional support, practical support), which are reliable structural constructs.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eWork-related factors (weekly working hours, monthly on-call frequency) and economic factors (monthly consumption) are independent risk factors for mental health problems.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSocial support plays a significant buffering role in the relationship between work stress and mental health.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe SEM confirms the causal paths: work stress negatively affects mental health, social support positively protects mental health, and mental health further affects quality of life.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Results Interpretation and Comparison\u003c/h2\u003e \u003cdiv id=\"Sec30\" class=\"Section3\"\u003e \u003ch2\u003e5.2.1 Mental Health Status: Global Commonality and Chinese Characteristics\u003c/h2\u003e \u003cp\u003eThe prevalence of moderate-to-severe depression and anxiety in this study is consistent with the global average (15%-30%) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], indicating that mental health problems among medical residents are a global challenge. However, the unique characteristics of Chinese medical residents are also evident: 83.89% have a monthly income\u0026thinsp;\u0026lt;\u0026thinsp;2000 RMB, significantly lower than that of European and American residents (average monthly income\u0026thinsp;\u0026gt;\u0026thinsp;3000 USD) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and 92.8% have night shifts (24.9% \u0026gt;7 times/month) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], forming a \"low income\u0026thinsp;+\u0026thinsp;high workload\" dual pressure pattern. This is different from Western studies that focus more on medical disputes and career burnout [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], reflecting the stage characteristics of China\u0026rsquo;s standardized training system [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Viseu et al. (2018) also found that economic pressure was a more prominent stressor for Chinese residents compared to their Western counterparts [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section3\"\u003e \u003ch2\u003e5.2.2 Weekly Working Hours and Monthly On-call Frequency as Risk Factors\u003c/h2\u003e \u003cp\u003eWeekly working hours and monthly on-call frequency are significant predictors of insomnia and anxiety, which is consistent with Obeng Nkrumah et al.\u0026rsquo;s (2025) global scoping review [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], confirming that excessive workload is a universal risk factor. The positive association between monthly consumption and mental health problems indicates that economic pressure exacerbates negative emotions. This is supported by Viseu et al. (2018), who found that economic stress factors were positively correlated with stress, anxiety and depression among medical staff [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In China, the contradiction between low income and basic consumption needs of medical residents is particularly prominent: the \u003cem\u003eReport on the Current Situation and Salary Consumption of Standardized Training for Resident Physicians in China (2023)\u003c/em\u003e reported that 60.04% of residents have monthly consumption of 1000\u0026ndash;1999 RMB, while 83.89% earn\u0026thinsp;\u0026lt;\u0026thinsp;2000 RMB, making economic pressure an important hidden factor affecting mental health [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section3\"\u003e \u003ch2\u003e5.2.3 The Buffering Effect of Social Support: Cross-cultural Verification and Dimensional Differences\u003c/h2\u003e \u003cp\u003eThis study confirms the buffering effect of social support, consistent with Cohen\u0026rsquo;s (2007) Stress-Buffering Model [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Emotional support and practical support both have protective effects, but family support and friend support have similar weights (β=-0.062 vs. β=-0.058). Notably, \"supervisor/colleague support\" did not enter the significant path, which differs from Western studies where colleague support is more prominent [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This may be due to the imperfect workplace support system for Chinese medical residents, where the hierarchical clinical culture makes it difficult to form effective peer support networks [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Lio et al. (2016) further pointed out that only 35.32% of Chinese residents hold a positive attitude towards their training departments, which may weaken the protective effect of workplace support [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Limitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eSingle-center sampling may limit generalizability; multi-center studies with diverse regions and hospital levels are needed to enhance external validity.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCross-sectional design cannot establish causal relationships; longitudinal studies should track mental health changes over training to clarify temporal associations.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSelf-reported data may have response bias (e.g., underreporting of severe symptoms); this study reduced bias through anonymous surveys, and future studies can add objective indicators such as physiological measurements (e.g., cortisol levels).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe study did not explore the differences in mental health status among different departments (e.g., emergency medicine vs. internal medicine), which may be a direction for further analysis.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Practical Implications and Suggestions\u003c/h2\u003e \u003cdiv id=\"Sec35\" class=\"Section3\"\u003e \u003ch2\u003e5.4.1 Mental Health Intervention Strategies\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003e\u003c/b\u003eReduce Work Burden: Hospitals should optimize work schedules, limit weekly working hours to \u0026le;\u0026thinsp;48 hours (in line with the National Health Commission\u0026rsquo;s Standardized Training for Residents Management Measures (2023 Edition) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]), and adopt a reasonable on-call rotation system (e.g., no more than 3 night shifts per week) to reduce unnecessary night shifts, thereby improving sleep quality [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eStrengthen Economic Support: Relevant departments should increase training subsidies, adjust monthly income to match the cost of living (e.g., linking subsidies to local consumption levels), and provide economic assistance for low-income residents to alleviate economic pressure [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eImprove Social Support Systems: Establish multi-dimensional support networks: family support (encourage regular communication between residents and their families), peer support (set up resident support groups for experience sharing and emotional mutual assistance), and supervisor support (train supervisors to identify early mental health signals and provide timely guidance) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec36\" class=\"Section3\"\u003e \u003ch2\u003e5.4.2 Sleep Health Targeted Interventions\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e Optimize Sleep Environment: Hospitals should equip on-call lounges with blackout curtains, earplugs, and comfortable beds to improve sleep conditions during shifts.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePopularize Sleep Health Education: Include sleep hygiene knowledge (e.g., avoiding electronic devices before bedtime, using relaxation techniques) in standardized training courses to help residents improve sleep self-management capabilities [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTargeted Support for High-risk Groups: For residents with high work stress and low social support, carry out one-on-one psychological counseling and social support connection services to reduce the risk of sleep disorders [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec37\" class=\"Section3\"\u003e \u003ch2\u003e5.4.3 Policy and System-level Support\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eAlign resident training subsidies with local minimum wage standards: According to the \u003cem\u003eChina Standardized Training for Residents Development Report (2022)\u003c/em\u003e, the current average monthly subsidy for residents is approximately 2400 RMB, which should be adjusted based on regional cost of living to reduce economic pressure [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEstablish a national occupational mental health monitoring system for medical residents: Regularly assess mental health status, set up early warning mechanisms for high-risk groups (e.g., emergency department residents with frequent night shifts), and incorporate mental health indicators into hospital management evaluations [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePromote workplace mental health education: Integrate stress management, sleep hygiene, and social support utilization skills into standardized training curricula, with at least 4 hours of specialized training per year [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEstablish a national occupational mental health monitoring system for medical residents: Regularly assess mental health status, set up early warning mechanisms for high-risk groups (e.g., emergency department residents with frequent night shifts), and incorporate mental health indicators into hospital management evaluations [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This aligns with the goal of standardized residency training to balance training quality and resident well-being [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec38\" class=\"Section2\"\u003e \u003ch2\u003e5.5 Future Research Directions\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eConduct longitudinal studies to track the dynamic changes of mental health during training (e.g., pre-training, mid-training, post-training) and clarify the causal relationships between factors [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eUse mixed-methods research (quantitative\u0026thinsp;+\u0026thinsp;qualitative) to explore the subjective experience of residents with severe mental health problems and identify potential hidden stressors (e.g., interpersonal conflicts, career confusion) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEvaluate the effectiveness of targeted interventions (e.g., social support groups, cognitive-behavioral therapy, mindfulness training) through randomized controlled trials to provide evidence-based basis for mental health promotion [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eExplore the differences in mental health status and influencing factors among different departments to develop personalized intervention strategies [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eMedical residents in China face significant mental health challenges, with moderate-to-severe anxiety and depression being prominent. Work burden, economic pressure, and lack of social support are key influencing factors, and social support can buffer the negative impact of work stress. The structural equation model confirms the chain relationship: work stress and social support affect mental health, which in turn influences quality of life. The scatter plot further verifies that social support can weaken the negative impact of work stress on sleep quality. Comprehensive strategies involving hospitals, educational institutions, and policymakers are needed to reduce work burden, improve economic support, and strengthen social support systems, thereby promoting the mental health and well-being of medical residents and ensuring the quality of clinical medical services. These findings provide actionable empirical evidence for optimizing China\u0026rsquo;s resident standardized training system.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDASS-21\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDepression Anxiety Stress Scale-21\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eISI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInsomnia Severity Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMSPSS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMultidimensional Scale of Perceived Social Support\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHOQOL-BREF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization Quality of Life-BREF\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSEM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStructural Equation Modeling\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the Institutional Review Board (IRB) of The Second Hospital of Shanxi Medical University on January 15, 2024 (Approval No.: 2024-012). All procedures involving human participants were in accordance with the Declaration of Helsinki. All participants were informed of the study purpose, data usage, and privacy protection measures prior to questionnaire completion. Since the survey was conducted anonymously via the Wenjuanxing platform (no personal identifiable information was collected), informed consent was obtained in the form of implied consent\u0026mdash;participants\u0026rsquo; voluntary completion and submission of the questionnaire were deemed as consent to participate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. No individual participant data or identifiable information is presented in the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe de-identified raw survey data (Supplementary File 2), complete R code for statistical analysis/visualization (Supplementary File 1), and English version of the self-designed questionnaire (Supplementary File 3) are available upon manuscript publication via the BMC Public Health online repository (https://bmcpublichealth.biomedcentral.com/). Prior to publication, the data are stored securely in a password-protected server of The Second Hospital of Shanxi Medical University, complying with the ethical guidelines for participant privacy protection (Ethical Approval No.: 2024-012 issued by the Ethics Committee of The Second Hospital of Shanxi Medical University).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests (financial or non-financial) related to the submitted work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received no specific funding from public, commercial, or non-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eX.Z. (Xingtao Zhao): Study design, data collection, statistical analysis, and draft manuscript writing;\u003c/p\u003e\n\u003cp\u003eX.W. (Xin Wang): Literature review, data cleaning, and manuscript revision;\u003c/p\u003e\n\u003cp\u003eX.R. (Xiajin Ren)*: Conceptualization, supervision, critical revision of the manuscript for important intellectual content, and final approval of the published version.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eReynolds CF 3rd, Clayton PJ, Commentary. Out of the silence: confronting depression in medical students and residents. Acad Med. 2009;84(2):159\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/ACM.0b013e31819397c7\u003c/span\u003e\u003cspan address=\"10.1097/ACM.0b013e31819397c7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID:19174657.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNair M, Moss N, Bashir A, Garate D, Thomas D, Fu S, Phu D, Pham C. 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Int J Health Plann Manage. 2020;35(2):592\u0026ndash;605. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/hpm.2970\u003c/span\u003e\u003cspan address=\"10.1002/hpm.2970\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2019 Nov 19. PMID:31742772.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Medical residents, Mental health, Dual pressure, Social support, Sleep quality, Occupational health, Cross-sectional study","lastPublishedDoi":"10.21203/rs.3.rs-8598823/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8598823/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObjective: This study aimed to assess the mental health status (depression, anxiety, stress, insomnia) of Chinese medical residents facing a unique \"low income + high workload\" dual pressure, identify key influencing factors, and explore the differential buffering effects of emotional and practical social support.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods: A cross-sectional survey was conducted among 469 medical residents (28.26% from internal medicine, 19.8% from surgery, 17.5% from emergency medicine, and 34.4% from other departments) using standardized scales (DASS-21, ISI, MSPSS, WHOQOL-BREF). Descriptive statistics, factor analysis, multiple linear regression, interaction effect tests, and structural equation modeling (SEM) were performed using SPSS 26.0, AMOS 24.0, and R 4.3.0 (packages: ggplot2 3.4.4, lavaan 0.6-16, factoextra 1.0.7).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults: The prevalence of moderate-to-severe anxiety, depression, and clinical insomnia was 15.8%, 11.9%, and 6.1%, respectively. Weekly working hours (β=0.193, p=0.007), monthly on-call frequency (β=0.126, p=0.015), and monthly consumption (β=0.143, p=0.003) were independent risk factors for insomnia. Social support significantly buffered the negative impact of work stress on mental health (interaction term β=-0.089, p\u0026lt;0.05), with emotional and practical support exerting similar protective effects (family support: β=-0.062; friend support: β=-0.058). The SEM showed good fit (χ²/df=2.371, RMSEA=0.054, CFI=0.928, TLI=0.917, SRMR=0.048), confirming the paths: work stress→mental health (β=0.412, p\u0026lt;0.001), social support→mental health (β=-0.326, p\u0026lt;0.001), and mental health→quality of life (β=0.684, p\u0026lt;0.001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusion: Chinese medical residents face severe mental health challenges driven by dual pressure. Reducing work burden, improving economic support, and constructing multi-dimensional social support systems are crucial for workplace mental health promotion, providing actionable empirical evidence for optimizing China’s resident standardized training system and public health policy-making.\u003c/p\u003e","manuscriptTitle":"Dual Pressure of Low Income and High Workload: Impact on Mental Health and Sleep Quality Among Chinese Medical Residents, and the Buffering Role of Social Support","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-02 09:28:37","doi":"10.21203/rs.3.rs-8598823/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-02-25T13:18:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-23T13:36:19+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-29T06:32:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-28T00:43:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-01-28T00:37:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"214e83d1-4a60-4b64-b5c6-8704c7c6e119","owner":[],"postedDate":"March 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-02T09:28:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-02 09:28:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8598823","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8598823","identity":"rs-8598823","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}