Background: Occupational burnout and fatigue are critical issues affecting the health and performance of healthcare workers (HCWs) globally. These outcomes are often driven by complex and overlapping work-related stressors, which remain insufficiently understood in combination. Objective: To investigate the associations of multiple work-related stressors with occupational burnout and fatigue, and to identify distinct stress patterns and critical stressors among HCWs. Method: A cross-sectional survey was conducted using a self-administered electronic questionnaire among 2695 HCWs in Shanghai, China. Validated questionnaire scales were used to assess work-related stress (self-developed occupational stress scale for medical staff, CSSM), occupational burnout (Maslach Burnout Inventory–General Survey, MBI-GS), and fatigue (Fatigue Scale-14, FS-14). Latent profile analysis (LPA) was employed to identify distinct work-related stress patterns. Generalized linear models (GLMs) were used to explore the associations between individual stressors, stress patterns, and occupational burnout and fatigue. Additionally, weighted quantile sum (WQS) models were utilized to evaluate the combined effects of multiple stressors and identify the main contributors. Results: In this study, 77.0% and 71.2% of participants were classified as experiencing occupational burnout and fatigue, respectively. A strained doctor–patient relationship was the highest-rated work-related stressor. All work-related stressors, including career development, interpersonal relationships, work–life imbalance, physical environment, doctor–patient relationship, social environment, and workload, were significantly associated with burnout (β: 0.444~0.956, p < 0.001) and fatigue (β: 1.384~3.404, p < 0.001). WQS assigned higher weights to career development and workload for burnout, and to workload and work–life imbalance for fatigue. LPA identified two distinct occupational stress patterns. HCWs characterized by higher stress levels in physical environment, career development, workload, and interpersonal relationships exhibited significantly higher burnout scores (β = 0.325, 95% CI: 0.122, 0.528), particularly in the reduced personal accomplishment (PA) dimension (β = 1.003, 95% CI: 0.746, 1.259). Conclusions: This study highlighted the high prevalence of occupational burnout and fatigue among HCWs in Shanghai, China. Occupational stressors were associated with both burnout and fatigue, with higher workload, work–life imbalance, and poorer career development showing particularly significant contributions. These findings emphasized the urgent need for targeted interventions, including workload management, career development programs, and mental health support, to reduce occupational stress and mitigate its adverse effects on HCWs.

Background: Sleep disorders are highly prevalent among healthcare workers (HCWs) globally, with occupational stress (OS) being a major underlying cause. Psychological capital (PsyCap) may serve as a protective resource, yet its combined relationship with OS and sleep quality remains underexplored. Objectives: We aimed to identify distinct OS-PsyCap profiles among HCWs, examine their associations with sleep quality, and investigate the mediating and moderating roles of PsyCap. Methods: A cross-sectional study was conducted among 1046 HCWs in Shanghai in December 2024. The Job Content Questionnaire, Psychological Capital Questionnaire, and Pittsburgh Sleep Quality Index were used to measure OS, PsyCap, and sleep quality, respectively. Latent profile analysis (LPA) was conducted to identify OS–PsyCap subgroups. Generalized linear models (GLM) and moderation and mediation analyses were performed to examine associations and to elucidate the role of PsyCap. Results: HCWs were divided into two distinct profiles: Class 1 (low OS and high PsyCap, 45.2%) and Class 2 (high OS and low PsyCap, 54.8%). HCWs in Class 2 had significantly poorer self-rated health and more sleep disturbances. Higher job demands and organizational support were associated with worse sleep outcomes, while higher autonomy and higher PsyCap were protective. In mediation and moderation analyses, PsyCap mediated 16.4–37.8% of OS–sleep relationships but also amplified OS effects on certain sleep outcomes under high stress. Conclusions: High OS with low PsyCap significantly increased sleep disorder risk among HCWs. PsyCap exhibited dual mechanisms, mediating stress effects while exacerbating sleep issues in high-stress contexts. Targeted interventions should address these HCWs in distinct profiles and pathways.