Predictive Analysis of Stress and Burnout Among Medical Students: Exploring Personal and Environmental Contributors
DOI:
https://doi.org/10.36283/ziun-pjmd14-3/051Keywords:
Stress, Burnout, Mindfulness, Perfectionism, Sleep Quality, Physical Activity, Self-Efficacy, Social SupportAbstract
Background: Medical education is widely recognized as a demanding and high-pressure environment. The intense academic workload, clinical responsibilities, and emotional challenges increase stress and the risk of burnout among medical students. This study aimed to identify key predictors and high-risk profiles for stress and burnout among medical students using multiple regression analysis and CHAID decision tree models.
Methods: A cross-sectional study was conducted on medical students (n = 250) from Bakhtawar Amin Medical and Dental College, Multan, and Nishtar Medical University, Multan Medical and Dental College, Pakistan, between April and October 2024, using a convenience sampling technique. The required sample size was calculated using G*Power 3.1 to ensure sufficient statistical power. Standardized instruments were used to assess stress (Perceived Stress Scale), burnout (Oldenburg Burnout Inventory), individual traits (BTPS-SF, GSE, BRS, MAAS), and contextual factors (WHOQOL-BREF), along with demographic variables. Data analysis was performed using IBM SPSS Statistics version 27.0. Multiple regression analyses were used to identify significant predictors, while a CHAID decision tree model classified high-risk subgroups. The level of significance was set at p < 0.05.
Results: Regression analysis revealed that perfectionism (p < 0.001) and low self-efficacy (p < 0.001) were the strongest predictors of stress, explaining 45% of the variance. For burnout, lack of physical activity (p = 0.002) and weak social support (p < 0.001) emerged as significant contributors (R² = 0.39, p < 0.001). The CHAID model identified sleep quality (p < 0.001) as a key moderator of stress, with the highest stress scores found among students with poor sleep and high perfectionism (M = 32.8, SD = 5.4). For burnout, the highest-risk group comprised physically inactive students lacking family support (M = 45.2, SD = 6.1, p < 0.001).
Conclusion: Psychological and physical health factors—including perfectionism, self-efficacy, sleep quality, physical activity, and social support—significantly influence stress and burnout among medical students. Interventions targeting these factors may help mitigate psychological distress and promote student well-being in medical training environments.
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