Cross-sectional survey identifies factors like occupational fatigue and job satisfaction influencing retirement support among healthcare workers in China, suggesting targeted policies are needed.
Amid growing concerns over healthcare workforce shortages in aging societies, delayed retirement has emerged as a strategic policy response. However, little is known about the determinants of retirement attitudes across different healthcare professions in middle-income countries. Guided by Role Theory and the Push-Pull Model, this study aimed to identify demographic, occupational, and psychosocial predictors of support for delayed retirement among healthcare workers in China, with attention to inter-professional variation. A cross-sectional survey was conducted among 1,200 full-time healthcare workers in Sichuan Province, including doctors, nurses, technicians, and administrative staff. A structured questionnaire captured data on demographics, work conditions, job satisfaction, occupational fatigue, self-rated health, and chronic illness. Univariate and multivariate logistic regression analyses were used to identify independent predictors of support for delayed retirement. Support for delayed retirement was positively associated with older age (OR: 1.06), male gender (OR: 1.34), higher education (OR: 1.42–1.65), longer working hours, more frequent night shifts, and higher job satisfaction (OR: 1.55), while greater occupational fatigue was negatively associated (OR: 0.82; all p < 0.01). Supporters reported better health, lower fatigue, and greater career engagement. Subgroup comparisons revealed marked differences in predictors and attitudes across professional roles, reflecting distinct role identities and workplace demands. By applying retirement theory to a diverse healthcare sample, this study highlights the need for differentiated workforce retention strategies. Findings suggest that policies should account for occupational fatigue, gendered caregiving burdens, and role-based professional motivations to ensure sustainable retirement planning in resource-constrained health systems.
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Sun et al. (2025) studied this question.
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