Journal of affective disorders · 2026
Risk configurations for anxiety symptoms among medical postgraduate students: A fuzzy-set qualitative comparative analysis.
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Huang Lei, Kou Jiahui, Zhang Yiwen, Chen Nian, Chen Jin, Zhou Xiaomin, Wu Yan
Аннотация
BACKGROUND: Medical postgraduate students face heightened risks for anxiety disorders due to intense academic and clinical demands. Previous studies, largely relying on regression models, may overlook the complex interplay of multiple risk factors. This study aims to identify combinatorial conditions (configurations) sufficient for anxiety symptoms using fuzzy-set qualitative comparative analysis (fsQCA).
METHODS: A cross-sectional survey was conducted among 1116 medical postgraduate students in China. Data on demographic, academic, work-related, lifestyle, and psychological factors were collected. Anxiety symptoms were assessed using the Generalized Anxiety Disorder 7-item scale (GAD-7). A series of multivariate linear regression models were fitted to examine variable associations preliminarily. Subsequently, fsQCA was employed to identify combinatorial conditions, analyzing five key factors: financial hardship (FH), long workdays (LW), physical exercise (PE), psychological resilience (PR), and adverse academic events (AAE).
RESULTS: No single condition was necessary for anxiety. Three sufficient configurations were identified, explaining 24.6% of cases (overall coverage = 0.246, consistency = 0.811): 1) FH × ~PR × AAE (consistency = 0.847); 2) FH × LW × ~PR (consistency = 0.855); 3) LW × ~PE × ~PR × AAE (consistency = 0.862). Low psychological resilience was a core component across all configurations.
LIMITATIONS: The cross-sectional design precludes causal inference, and the single-center sample may limit generalizability.
CONCLUSIONS: Anxiety symptoms among medical postgraduate students arises from distinct combinations of academic, work patterns, and personal factors, rather than from any single factor. Interventions could consider moving beyond isolated variables to address synergistic risk profiles, with resilience training as a potential component.
METHODS: A cross-sectional survey was conducted among 1116 medical postgraduate students in China. Data on demographic, academic, work-related, lifestyle, and psychological factors were collected. Anxiety symptoms were assessed using the Generalized Anxiety Disorder 7-item scale (GAD-7). A series of multivariate linear regression models were fitted to examine variable associations preliminarily. Subsequently, fsQCA was employed to identify combinatorial conditions, analyzing five key factors: financial hardship (FH), long workdays (LW), physical exercise (PE), psychological resilience (PR), and adverse academic events (AAE).
RESULTS: No single condition was necessary for anxiety. Three sufficient configurations were identified, explaining 24.6% of cases (overall coverage = 0.246, consistency = 0.811): 1) FH × ~PR × AAE (consistency = 0.847); 2) FH × LW × ~PR (consistency = 0.855); 3) LW × ~PE × ~PR × AAE (consistency = 0.862). Low psychological resilience was a core component across all configurations.
LIMITATIONS: The cross-sectional design precludes causal inference, and the single-center sample may limit generalizability.
CONCLUSIONS: Anxiety symptoms among medical postgraduate students arises from distinct combinations of academic, work patterns, and personal factors, rather than from any single factor. Interventions could consider moving beyond isolated variables to address synergistic risk profiles, with resilience training as a potential component.