Nan L, Wang Y, Wareesawetsuwan N, Sanan M, Zhao XF, Yang L, Zhu WL. Association between job burnout and depressive symptoms in rural primary healthcare workers: The indirect role of sleep quality. World J Psychiatry 2026; 16(10): 122230 [DOI: 10.5498/wjp.122230]
Corresponding Author of This Article
Lei Yang, MD, PhD, Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshe East Road, Zhengzhou 450000, Henan Province, China. 355280249@qq.com
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Nan L, Wang Y, Wareesawetsuwan N, Sanan M, Zhao XF, Yang L, Zhu WL. Association between job burnout and depressive symptoms in rural primary healthcare workers: The indirect role of sleep quality. World J Psychiatry 2026; 16(10): 122230 [DOI: 10.5498/wjp.122230]
Lian Nan, Muhammad Sanan, Xiao-Feng Zhao, Lei Yang, Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, Henan Province, China
Ye Wang, Department of Psychiatry, Shenyang Mental Health Center, Shenyang 110000, Liaoning Province, China
Nicha Wareesawetsuwan, Department of Medicine Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02108, United States
Wen-Li Zhu, Department of Psychiatry, The Fourth People’s Hospital of Wuhu, Wuhu 241002, Anhui Province, China
Co-corresponding authors: Lei Yang and Wen-Li Zhu.
Author contributions: Nan L contributed to conceptualization, study design, project coordination, data interpretation, supervision, and drafting of the original manuscript; Wang Y contributed to data collection, data curation, preliminary analysis, and manuscript revision; Wareesawetsuwan N contributed to manuscript review, editing, and critical revision for important intellectual content; Sanan M contributed to data analysis, data interpretation, and manuscript revision; Zhao XF contributed to data acquisition, investigation, and data verification; Yang L contributed to data acquisition, statistical support, and data interpretation; Zhu WL contributed to study supervision, project administration, data interpretation, and critical revision of the manuscript. All authors read and approved the final manuscript. Yang L and Zhu WL were designated as co-corresponding authors because they made complementary and substantial contributions to the study and share responsibility for its integrity and communication. Yang L contributed significantly to data acquisition, statistical support, interpretation of the results, and coordination of issues related to the dataset and analytical methods. Zhu WL provided overall study supervision, project administration, interpretation of the findings, and critical revision of the manuscript for important intellectual content. Their respective expertise enables them to respond appropriately to questions concerning both the methodological and administrative aspects of the study. In addition, they represent the principal institutions involved in conducting and supervising the research. Both authors approved the final version of the manuscript and agreed to share responsibility for correspondence with the journal, responses to reviewers, provision of study materials, and any questions arising after publication. Therefore, co-corresponding authorship accurately reflects their shared leadership, accountability, and continuing responsibility for this work.
AI contribution statement: During the preparation of this manuscript, artificial intelligence assisted tool (Chatgpt) was used only for language polishing and grammar improvement. The authors reviewed, edited, and approved all AI assisted content and take full responsibility for the accuracy, integrity, and originality of the manuscript.
Supported by Anhui Province Clinical Medical Research Transformation Special Project, No. 2022-04295107020065, No. 2022-04295107020006, and No. 2022-04295107020028; and Henan Province Science and Technology Research and Development Program, No. 242102311050.
Institutional review board statement: This study was approved by the Institutional Review Board of the Ethical Committee of the Fourth People's Hospital of Wuhu, No.[2023]-KY-18.
Informed consent statement: The requirement for written informed consent was waived by the Institutional Review Board of the Ethical Committee of the Fourth People's Hospital of Wuhu because this was an anonymous questionnaire based observational study involving minimal risk. Participation was voluntary, and completion of the questionnaire was considered implied informed consent.
Conflict-of-interest statement: The authors declare that they have no conflicts of interest.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Data sharing statement: The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.
Corresponding author: Lei Yang, MD, PhD, Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshe East Road, Zhengzhou 450000, Henan Province, China. 355280249@qq.com
Received: April 16, 2026 Revised: May 28, 2026 Accepted: August 25, 2026 Published online: October 19, 2026 Processing time: 180 Days and 7.2 Hours
Abstract
BACKGROUND
Primary healthcare workers (PHWs) in township health centers are the backbone of rural healthcare in China. High rates of job burnout and depression among PHWs adversely affect patient care quality and increase turnover, contributing to the persistent shortage of PHWs. Although job burnout is a recognized risk factor for depression, the underlying mechanisms are not fully understood.
AIM
To examine the association between job burnout, depressive symptoms, and sleep quality among PHWs, and to explore whether sleep quality may partly explain the association between job burnout and depressive symptoms.
METHODS
A cross-sectional study was conducted between January and September 2023 among PHWs working in township health centers in Anhui Province, China. A total of 450 self-administered questionnaires were distributed, collecting data on sociodemographic characteristics, job burnout (using the Maslach Burnout Inventory), depressive symptoms (using the Self-Rating Depression Scale), and sleep quality (using the Pittsburgh Sleep Quality Index). Descriptive statistics, Spearman correlation analysis, structural equation modeling, and the bootstrap method were employed.
RESULTS
A total of 421 valid responses were collected, with an effective response rate of 93.6%. The mean age of participants was 37.7 ± 10.1 years, and 61.3% were female. Job burnout, depressive symptoms, and most components of sleep quality were significantly positively correlated. In the exploratory mediation model, job burnout showed a significant total association with depressive symptoms, B = 0.792 [95% confidence interval (CI): 0.638-0.950]. After accounting for sleep quality, the direct association between job burnout and depressive symptoms remained significant, B = 0.589 (95%CI: 0.418-0.768). Sleep quality showed a significant indirect association in the relationship between job burnout and depressive symptoms, B = 0.203 (95%CI: 0.110-0.318), accounting for 25.6% of the total association.
CONCLUSION
Sleep quality may partly explain the association between job burnout and depressive symptoms among PHWs. These findings underscore the need for strategies that address sleep quality and job burnout to help reduce depressive symptoms among PHWs, although causal inference cannot be made due to the cross-sectional design.
Core Tip: This cross-sectional study among rural primary healthcare workers demonstrates that sleep quality partially mediates the relationship between job burnout and depressive symptoms, accounting for 25.6% of the total effect. These findings highlight sleep disturbance as a modifiable pathway linking occupational stress to depression. Targeting sleep quality may offer a practical and scalable strategy to reduce depressive symptoms and improve workforce retention in resource-limited primary care settings.
Citation: Nan L, Wang Y, Wareesawetsuwan N, Sanan M, Zhao XF, Yang L, Zhu WL. Association between job burnout and depressive symptoms in rural primary healthcare workers: The indirect role of sleep quality. World J Psychiatry 2026; 16(10): 122230
Primary healthcare workers (PHWs) include all individuals involved in delivering primary care services, such as volunteers, nurses, village doctors, general practitioners, pharmacists, and policy-makers, as defined by the World Health Organization. PHWs working in township health centers form the backbone of China’s rural health system. They serve as the first point of contact for individuals seeking healthcare, a role that has increasingly gained importance since China’s 2009 healthcare reform, which prioritized equitable access to healthcare, particularly in rural areas[1,2].
Although primary healthcare has become a national priority, the shortage of PHWs in rural China remains a persistent challenge due to high turnover rates[3]. Reports indicate that approximately one in three doctors in township health centers intends to leave their job[1,4].
Job burnout is an occupational syndrome characterized by emotional exhaustion (Emo), depersonalization, and reduced personal accomplishment (Acc)[5,6]. More than half of PHWs have reported experiencing job burnout[7]. Compared to other healthcare professionals, primary care doctors consistently report the highest levels of burnout, even after the coronavirus disease 2019 pandemic subsided in 2023[8]. PHWs in rural areas often face high workloads, limited resources, low income, and few opportunities for career advancement[9,10], all of which contribute to job burnout[11]. Burnout is significantly associated with an increased intention to quit and a decline in the quality of care[12-14].
Depression is also prevalent among PHWs, with rates ranging from 42.6% to 50.7%[15,16], exceeding those seen in the general population. Work-related stressors are frequently cited as contributing factors[17]. Depression can adversely affect patient care and increase the risk of medical errors[18-20]. Although depression and burnout share features such as low motivation, they are distinct conditions[21,22]. Burnout is predominantly related to work, whereas depression affects multiple areas of life. It is well-established that burnout is a strong predictor of depression[23-25], yet the underlying mechanisms remain unclear.
Poor sleep quality is also common among healthcare workers, with a reported prevalence of 39.2% to 70.4%[26,27], often linked to long working hours and shift work. Sleep disturbances (Dis) are closely associated with both burnout and depression[28-31]. While several studies have identified sleep quality as a mediator between various stressors and depression[32-34], limited evidence is available regarding whether sleep quality helps explain the association between job burnout and depressive symptoms among rural PHWs.
Increasing the number of PHWs alone is not a sustainable solution to the shortage in primary healthcare. It is equally important to promote retention and support the mental well-being of the existing workforce. To address this gap, the present study examined the associations among job burnout, sleep quality, and depressive symptoms among PHWs in rural China, and explored whether sleep quality showed an indirect association between job burnout and depressive symptoms. By focusing on PHWs in township health centers, this study adds evidence from an understudied workforce and may inform future mental health support strategies aimed at improving sleep health, reducing burnout, and supporting workforce retention.
MATERIALS AND METHODS
Study design and participants
A cross-sectional study was conducted at the Fourth People’s Hospital of Wuhu in Anhui Province, between January and September 2023. Participants were selected using convenience sampling from PHWs working at township health centers in Anhui Province, who participated in routine psychological examinations at the Fourth People’s Hospital of Wuhu. The inclusion criteria were: (1) Employment at township health centers; (2) At least one year of work experience; and (3) Willingness to complete the questionnaire after being informed. The exclusion criteria included: (1) Apersonal or family history of mental illness; (2) Uncontrolled physical illnesses such as severe cardiovascular or respiratory disease; and (3) Refusal to complete the questionnaire after being informed.
Self-administered paper-based questionnaires were distributed to participants after written informed consent was obtained. The questionnaire required approximately 20 minutes to complete. An investigator was present on site to address any questions. A total of 450 questionnaires were administered, and 437 were returned, resulting in a response rate of 97.1%. Of these, 16 questionnaires were excluded because they contained missing data on key study variables required for the correlation, structural equation modeling, and mediation analyses. Because the proportion of excluded questionnaires was small, 16 of 437 returned questionnaires, or 3.7%, complete-case analysis was performed and multiple imputation was not conducted. Ultimately, 421 valid questionnaires were collected, corresponding to an effective questionnaire rate of 93.6%.
Measurements
In this study, a structured questionnaire was developed based on a comprehensive literature review and previously validated instruments, with modifications for our study context. The questionnaire comprised four sections: (1) Sociodemographic characteristics; (2) Depression; (3) Job burnout; and (4) Sleep quality.
Sociodemographic characteristics: Six sociodemographic characteristics were collected: Age, gender, marital status, educational level, years of work experience, and monthly income.
Job burnout assessment: Job burnout was assessed using the Maslach Burnout Inventory-General Survey[6]. The Chinese version revised by Li et al[35] was used in this study. The instrument includes 15 items measuring three dimensions of burnout: (Emo, 5 items), cynicism (Cyn, 4 items), and (Acc, 6 items). Each item is rated on a seven-point frequency scale ranging from 0 (never) to 6 (daily). A total burnout score (TBS) was calculated as the sum of Emo and Cyn plus the reversed score of Acc. Higher TBS indicates higher level of burnout. In this study, the Cronbach’s alpha was 0.757, indicating acceptable internal consistency.
Depression assessment: Depressive symptoms were evaluated using the Zung Self-Rating Depression Scale (SDS)[36,37], a well-established and widely utilized tool for assessing depression in adults. In this study, the validated Chinese version of the SDS was employed. The scale consists of 20 items assessing depressive symptoms experienced over the past week. Each item is rated on a four-point Likert scale ranging from 1 (a little of the time or none) to 4 (most or all of the time). The raw score ranges from 20 to 80. Higher scores indicate more severe depressive symptoms. For the Chinese population, recommended cut-off index scores for depression are 50 or 53[38,39]. In this study, the Cronbach’s alpha was 0.890, indicating acceptable internal consistency.
Sleep quality assessment: Sleep quality was measured using the Chinese version of the Pittsburgh Sleep Quality Index (PSQI)[40,41]. This instrument evaluates sleep quality and disturbances over the previous month using 19 items that assess seven components: Subjective sleep quality (Qua), sleep latency (Lat), sleep duration (Dur), habitual sleep efficiency (Eff), Dis, use of sleep medication (Med), and daytime dysfunction (Dys)[42]. Each component is scored on a four-point scale ranging from 0 (no difficulty) to 3 (severe difficulty), and all component scores are summed to obtain a global score ranging from 0 to 21, with higher scores indicating poorer sleep quality. For the Chinese population, a PSQI global score > 7 is recommended as the cut-off for poor sleep quality, while scores ≤ 7 indicate good sleep quality[43]. In this study, the Cronbach’s alpha was 0.842, indicating acceptable internal consistency.
Statistical analysis
All statistical analyses were conducted using IBM SPSS Statistics for Windows, Version 22.0 (IBM Corp., Armonk, NY, United States) and IBM SPSS AMOS, Version 26.0. A two-tailed P value < 0.05 was considered statistically significant. Categorical variables were summarized as n and (%). Continuous variables were presented as mean ± SD or median with interquartile range, as appropriate. The Kolmogorov-Smirnov test was used to assess the normality of continuous data.
For group comparisons involving non-normally distributed variables, the Mann-Whitney U test was used for two-group comparisons and the Kruskal-Wallis H test for comparisons across more than two groups. The internal consistency of the measurement scales was evaluated using Cronbach’s alpha coefficient, with acceptable values ≥ 0.70[44]. Common method bias was examined using Harman’s single-factor test to assess potential bias due to self-reported data.
Spearman’s rank correlation was used for preliminary analysis of relationships among job burnout, sleep quality, and depressive symptoms. Structural equation modeling (SEM) was then conducted using the maximum likelihood estimation method. Model fit was evaluated using multiple indices: χ2, χ2/degrees of freedom ratio (χ2/df), comparative fit index (CFI), Tucker-Lewis index (TLI), incremental fit index (IFI), normed fit index (NFI), and root mean square error of approximation (RMSEA). Acceptable model fit was defined as χ2/df < 3, CFI, TLI, IFI, and NFI > 0.90, and RMSEA < 0.08[45].
An exploratory mediation analysis was conducted to examine whether sleep quality was involved in the association between job burnout and depressive symptoms. Bootstrapping with 5000 resamples was used to estimate the indirect association, which was considered statistically significant if the 95% confidence interval (CI) did not include zero.
A sample size estimation was performed using G*Power[46] as an approximate power assessment for the planned association analyses. Using F tests for linear multiple regression with fixed model R2 increase, the parameters were set as follows: Effect size f2 = 0.02, α error = 0.05, power = 0.80, 1 tested predictor, and 8 total predictors, including job burnout and the seven PSQI components. The minimum required valid sample size was 395 participants. After accounting for an anticipated 10% refusal or invalid questionnaire rate, the target sample size was approximately 439 participants. In the present study, 450 questionnaires were distributed and 421 valid questionnaires were included, exceeding the minimum required valid sample size.
Ethical considerations
This study was approved by the Institutional Review Board of the Ethical Committee of the Fourth People’s Hospital of Wuhu, approval No.[2023]-KY-18, and was conducted in accordance with the Declaration of Helsinki and the institutional Code of Ethics. The requirement for written informed consent was waived by the Institutional Review Board of the Ethical Committee of the Fourth People’s Hospital of Wuhu. Participation was voluntary, and completion of the anonymous questionnaire was considered implied informed consent.
RESULTS
Common method bias test
Common method bias was assessed using Harman’s single-factor test. The exploratory factor analysis revealed that 12 factors had eigenvalues greater than 1, and the explanatory variation rate of the first factor accounted for 25.37% of the total variance, which is below the accepted threshold of 50%[47]. These findings indicate that common method bias is not a significant concern in this study.
Sociodemographic characteristics and their associations with job burnout, depressive symptoms, and sleep quality
This study included 421 PHWs working in township health centers. The participants had a mean age of 37.7 ± 10.1 years. The majority were aged ≤ 30 years (31.8%), female (61.3%), married (77.7%), held a bachelor’s degree (72.7%), had more than 20 years of working experience (34.9%), and reported a monthly income between 3001 and 5000 CNY (51.8%). The median scores for the TBS, SDS, and PSQI were 22.0 (12.0-35.0), 33.0 (29.0-40.0) and 5.0 (3.0-7.0), respectively. Based on established cut-off criteria, 66.27% (279/421) of participants met the criterion for job burnout, 2.85% (12/421) screened positive for probable depression, and 24.23% (102/421) had poor sleep quality.
Age was significantly associated with both job burnout (P = 0.001) and depressive symptoms (P = 0.021). Participants aged ≤ 30 years reported the highest levels of burnout and depressive symptoms. Notably, TBS and depressive symptoms showed a decreasing trend with increasing age, with the lowest median scores observed among participants aged 41-50 years. Marital status was also significantly associated with job burnout (P = 0.036), with single participants reporting higher median burnout scores than their married counterparts [27.5 (15.3-39.8) vs 21.0 (10.0-33.0)]. Educational level was significantly associated with job burnout (P = 0.038) and sleep quality (P = 0.005).
A significant association was observed between working years and job burnout (P = 0.001), with the highest levels of burnout found among those with 6-10 years of work experience, followed by those with ≤ 5 years. Conversely, those with over 20 years of experience reported the lowest burnout levels. Monthly income was significantly associated with sleep quality (P = 0.003). Participants earning less than 2000 CNY per month experienced the poorest sleep quality, while those earning more than 5000 CNY had the highest sleep quality [38.0 (32.0-45.0) vs 31.0 (27.0-37.0)]. Further details are provided in Table 1.
Table 1 Sociodemographic characteristics of the participants.
Correlation among job burnout, depressive symptoms, and sleep quality
Spearman’s correlation analysis revealed significant positive associations among TBS, SDS, and most components of PSQI, including Qua, Lat, Dur, Med, Eff, Dis, and Dys (Table 2). A strong positive correlation was observed between TBS and SDS (r = 0.537, P < 0.01).
Table 2 Spearman’s correlation coefficients of job burnout, depressive symptoms, and sleep quality.
TBS was significantly correlated with all PSQI components except for Med. The strongest positive correlations were observed with Dys (r = 0.462, P < 0.01) and Qua (r = 0.310, P < 0.01). Similarly, all PSQI components demonstrated significant positive correlations with SDS, with the strongest correlation also found with Qua (r = 0.442, P < 0.01), followed by Dys (r = 0.439, P < 0.01) and Eff (r = 0.377, P < 0.01). In addition, intra-correlations among the PSQI components were significant in most cases. Notably, Qua showed strong correlations with Lat (r = 0.565, P < 0.01) and Dys (r = 0.534, P < 0.01). These findings support the close associations among job burnout, depressive symptoms, and sleep quality.
Model fit indices of unconstrained model and constrained (nested) model
We developed a structural equation model to examine the possible indirect association of sleep quality in the relationship between job burnout and depressive symptoms. To evaluate model fit, we compared the unconstrained model with a constrained (nested) model. The unconstrained model demonstrated a good fit to the data, with all fit indices falling within the acceptable ranges. In contrast, the constrained model showed a poorer fit, as indicated by χ2/df and RMSEA values exceeding acceptable ranges. The χ2 difference test yielded a Δχ2 (Δdf) of 107.58[6], indicating a statistically significant deterioration in model fit when constraints were applied. These findings suggest that the unconstrained model provides a better representation of the data (Table 3).
Table 3 Comparison of model fit indices between unconstrained and constrained (nested) models.
Direct associations among job burnout, sleep quality and depressive symptoms in the unconstrained model
As shown in Table 4 and Figure 1, path analysis showed significant direct associations between TBS and all PSQI components. TBS was positively and significantly associated with Qua (B = 0.050, P < 0.001), Lat (B = 0.043, P < 0.001), Dur (B = 0.029, P < 0.001), Med (B = 0.017, P = 0.006), Eff (B = 0.029, P < 0.001), Dis (B = 0.012, P = 0.003), and Dys (B = 0.093, P < 0.001). These results indicate that higher levels of job burnout are consistently associated with poorer outcomes across all domains of sleep quality.
Figure 1 Structural equation model showing the unconstrained pathways between job burnout, sleep quality, and depressive symptoms.
This model examines the direct and indirect effects of total burnout score (TBS) on the Self-Rating Depression Scale, with seven sleep quality variables serving as mediators. The TBS consists of three components: Emotional exhaustion, cynicism, and reduced personal accomplishment. The seven mediators are derived from the Pittsburgh Sleep Quality Index and include: Subjective sleep quality), sleep latency, sleep duration, use of sleep medication, sleep efficiency, sleep disturbance, and daytime dysfunction. Paths a1-a7 represent the associations between job burnout and each sleep component. Paths b1-b7 represent the associations between each sleep component and depressive symptoms. Path d1 represents the direct effect of job burnout on depressive symptoms, controlling for the mediating effects of sleep problems. TBS: Total burnout score; SDS: Self-Rating Depression Scale; Emo: Emotional exhaustion; Cyn: Cynicism; Acc: Reduced personal accomplishment; Qua: Subjective sleep quality; Lat: Sleep latency; Dur: Sleep duration; Med: Use of sleep medication; Eff: Sleep efficiency; Dis: Sleep disturbance; Dys: Daytime dysfunction.
Table 4 Path coefficients of the unconstrained model.
Regarding the direct associations between PSQI components and SDS, significant positive associations were observed for Qua (B = 1.580, P = 0.004), Lat (B = 1.047, P = 0.018), and Eff (B = 2.359, P < 0.001). However, Dur, Med, Dis, and Dys were not significantly associated with SDS in this model.
Indirect association of sleep quality between job burnout and depressive symptoms
As shown in Table 5, job burnout showed a significant total association with depressive symptoms, with an estimated coefficient of B = 0.792 (95%CI: 0.638-0.950, P < 0.001). After accounting for PSQI components, the direct association between job burnout and depressive symptoms remained significant (B = 0.589, 95%CI: 0.418-0.768, P < 0.001). The mediation analysis suggested a significant indirect association through PSQI components (B = 0.203, 95%CI: 0.110-0.318, P < 0.001), accounting for 25.6% of the total association.
Table 5 The indirect association of sleep quality in the relationship between job burnout and depressive symptoms.
Among the PSQI components, Qua (B = 0.079, 95%CI: 0.029-0.147, P = 0.003), Lat (B = 0.045, 95%CI: 0.008-0.099, P = 0.015), and Eff (B = 0.069, 95%CI: 0.028-0.133, P = 0.001) showed significant indirect associations between job burnout and depressive symptoms. Other components, including Dur, Med, Dis, and Dys, did not show significant indirect associations. These results suggest that some components of sleep quality may partly explain the association between job burnout and depressive symptoms.
DISCUSSION
This study examined the association among job burnout, sleep quality, and depressive symptoms among PHWs in township health centers. Our findings showed that sleep quality had a significant indirect association in the relationship between job burnout and depressive symptoms. To our knowledge, this is the first study to explore this relationship among PHWs working in rural China.
In this study, higher TBSs were positively correlated with greater depressive symptom severity, consistent with previous studies[22,48-50]. Fond et al[18] reported that burnout is among the strongest risk factors for depression in healthcare workers. Previous longitudinal studies have also suggested a temporal relationship between burnout and subsequent depressive symptoms, including studies among dentists[25] and a large cohort of 1632 employees[51]. Conversely, a prior history of depression has been reported to predict all dimensions of later job burnout[52], suggesting that depressive symptoms may intensify perceived exhaustion, sleep problems, and work-related distress[53]. Hatch et al[54] also found that job burnout and depressive symptoms changed in the same direction over 12 months of follow-up, supporting the possibility of a bidirectional relationship. Among rural PHWs, high workload, limited career advancement, inadequate compensation, and workforce shortages may be important contextual factors associated with both burnout and depression[7,55].
Moreover, our study revealed a positive correlation between job burnout and poor sleep quality. Prior literature has supported a bidirectional relationship between these two variables[56,57]. Healthcare professionals with higher burnout levels often report poorer sleep quality, as seen in doctors[58], nurses[59], and general healthcare workers[60]. PHWs working night shifts are particularly vulnerable due to disrupted sleep schedules. Burnout itself may cause physical and Emo, impairing the ability to achieve restorative sleep, while poor sleep, in turn, reduces coping capacity, perpetuating the burnout cycle.
We also found a significant association between poor sleep quality and depressive symptoms. This finding is consistent with prior evidence showing a strong association between Dis and depression[61-64], particularly among healthcare workers, with similar strong associations reported in studies from Ethiopia[65], Turkey[66], France[67], and Mexico[68]. Poor sleep impairs emotional regulation and may contribute to depression[69,70]. Nguyen et al[71] also reported that poor sleep predicts depression, potentially due to shared risk factors such as repetitive negative thinking and unhealthy lifestyle behaviors. Neurobiological mechanisms may also be involved, including disruption of serotonergic and dopaminergic systems. Conversely, depression may suppress melatonin production and disturb circadian rhythms[72,73], suggesting that the relationship between poor sleep quality and depressive symptoms may be bidirectional.
Our mediation analysis suggested that poor sleep quality had a significant indirect association between job burnout and depressive symptoms, accounting for 25.6% of the total association. This is comparable to other studies that examined sleep quality as a mediator. For example, Liu et al[33] reported a 13.3% mediating effect between loneliness and depression in middle-aged and older adults. Similarly, sleep quality mediated the relationship between problematic phone use and depression in college students (29%)[74], between family health and depression[75], and between cognitive decline and depression in older adults[76]. Together, these findings suggest that sleep quality may be an important factor linking psychosocial stressors and depressive symptoms. In the present study, poor sleep quality may help explain part of the association between job burnout and depressive symptoms.
Among the PSQI components, only Qua, Lat, and Eff showed significant indirect associations between job burnout and depressive symptoms. This pattern suggests that the association may be driven mainly by perceived poor sleep and insomnia-related difficulties, rather than sleep quantity alone. This interpretation is consistent with depression-related research showing that individual PSQI domains may differ in clinical relevance, with some sleep components improving after depression treatment while others do not[77]. It is also supported by evidence from orthopedic nurses, in whom Lat, Eff, and Qua showed strong correlations with burnout components[78], and by a student study showing that sleep quality and Lat were indirectly associated with academic burnout through perceived stress[79].
The practical implications of our findings are significant. Burnout, depressive symptoms, and poor sleep are highly prevalent among PHWs in township health centers and are closely interconnected. Poor sleep may be involved in the association between burnout and depressive symptoms, forming a pattern that could undermine the well-being and retention of rural healthcare providers. As China’s healthcare system faces increasing demands from an aging population and rising chronic disease burden, especially in rural areas, ensuring the mental health of PHWs is essential.
Although various policies, such as financial incentives and professional title awards, have been introduced to improve rural workforce retention, mental health remains under-addressed. A nationwide screening initiative targeting job burnout, depression, and sleep quality is urgently needed. Additionally, forming multidisciplinary teams to develop tailored mental health interventions for each health center may provide sustainable solutions. Losing rural PHWs could undermine the stability of primary healthcare in China.
Our study has several limitations. First, due to its cross-sectional design, causal relationships and temporal ordering among job burnout, sleep quality, and depressive symptoms cannot be established. Therefore, the proposed mediation pathway should be interpreted as exploratory and requires validation in prospective longitudinal studies before any causal claims can be made. Second, the use of clinic-based convenience sampling from PHWs who participated in routine psychological examinations at a single health center may introduce selection bias and limit the generalizability of the findings. Participants who attended psychological screening may differ systematically from the broader PHW population, particularly in terms of mental health concerns or willingness to participate in health assessments. Third, the exclusion of individuals with a personal or family history of mental illness may have truncated the range of depressive symptoms and led to underestimation of the associations among job burnout, sleep quality, and depressive symptoms. Fourth, although common method bias was not found to be a major concern, the use of self-reported questionnaires may still introduce response bias. Fifth, the median SDS score in our sample was below the Chinese cut-off for probable depression, indicating that most participants had relatively low levels of depressive symptoms. Therefore, the findings should be interpreted as associations with depressive symptom severity on a continuum rather than clinically diagnosed depression, which may limit the clinical interpretability of the results. Sixth, job burnout was analyzed using the TBS as an observed composite variable. Although this approach captures overall burnout, it does not show which specific burnout dimensions are most closely associated with sleep quality and depressive symptoms. Future studies should examine Emo, Cyn, and Acc separately. In addition, prospective longitudinal studies with more representative samples from village clinics, township health centers, and county hospitals are needed to validate the proposed pathway and improve generalizability.
CONCLUSION
Our findings showed a significant association between job burnout and depressive symptoms, with sleep quality showing a significant indirect association in this relationship. Sleep quality may partly explain the association between job burnout and depressive symptoms among PHWs. These findings underscore the need for strategies that address both job burnout and Dis to help reduce depressive symptoms among PHWs.
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The Integration of Work and Learning: Tackling the Complexity with Structural Equation Modelling. In: Harteis C, Rausch A, Seifried J, editors. Discourses on Professional Learning. Professional and Practice-based Learning. Springer, Dordrecht, 2014.
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Fuller CM, Simmering MJ, Atinc G, Atinc Y, Babin BJ. Common methods variance detection in business research.J Business Res. 2016;69:3192-3198.
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Claustrat B, Chazot G, Brun J, Jordan D, Sassolas G. A chronobiological study of melatonin and cortisol secretion in depressed subjects: plasma melatonin, a biochemical marker in major depression.Biol Psychiatry. 1984;19:1215-1228.
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