Background A high attrition rate in medical students has exacerbated the physician shortage in China. However, few studies have explored the risk factors of dropout intention in medical postgraduates. This study compared the prevalence of dropout intention and mental distress between medical and non-medical postgraduates in China and investigated risk factors for dropout intention. This study also explored the impact of medical postgraduates’ perception of the Chinese healthcare environment on their mental status and dropout intention. Methods This cross-sectional study was conducted using online questionnaires from October 2020 to April 2021. Convenience sampling was used to recruit postgraduates in different majors. Outcomes included dropout intention and potential risk factors, including mental distress, quality of life, and fatigue. Medical postgraduates were additionally assessed for healthcare environment satisfaction, burnout, career choice regret, and experiences of workplace violence. A logistic regression model was constructed to evaluate the association between dissatisfaction, mental distress, and turnover intention. Results A total of 740 medical and 670 non-medical postgraduates participated in the survey. The rates of depression symptoms (33.8% vs. 39.0%, p < 0.001), anxiety symptoms (22.2% vs. 32.4%, p < 0.001), and somatic symptoms (34.7% vs. 42.4%, p = 0.004) were lower in medical postgraduates, while more medical postgraduates (58.4% vs. 48.4%, p < 0.001) reported dropout intention. Dissatisfaction with the healthcare environment (odds ratio [OR]: 1.65; 95% confidence interval [CI]: 1.17–2.34, p = 0.005), career choice regret (OR: 6.23; 95% CI: 4.42–8.78, p < 0.001), and high perceived stress (OR: 2.74; 95%CI: 1.90–3.94, p < 0.001) remained independently associated with turnover intention. Conclusions Mental distress is common among postgraduates, calling for timely interventions. Medical postgraduates reported higher turnover intention. Healthcare environment perception also affected the mental health and dropout intentions of medical students. A decent future income, reduced workload, shorter duration medical training, and better doctor-patient relationships are urgently needed.

Background: Burnout, depression, and anxiety are highly prevalent among medical students, which often leads to their attrition. We aim to assess the inter-relationships of depression, burnout, and anxiety symptoms with dropout intention among Chinese medical undergraduates using the network analysis. Method: A total of 3,648 Chinese medical undergraduates were recruited through snowball sampling. Learning burnout scale, 9-item Patient Health Questionnaire (PHQ-9), and General Anxiety Disorder Scale (GAD-7) was used to assess burnout, depression, and anxiety symptoms, respectively. We used the EBICglasso model to estimate the network. We compared the network based on gender, study phase, and clinical experience. Results: After removing repeated submissions and incorrect responses to the trap question, 3,536 participants were included in the final analysis. The prevalence of burnout, depression, anxiety, and dropout intention was 38, 62.7, 38.4, and 39% respectively, which is consistent with previous findings. Network analysis suggested that anxiety and depression items clustered together and displayed several strong bridge connections, while burnout items formed another cluster. All the strongest edges were within the respective distress. Cynicism symptoms ‘I am fed up with study’ and ‘I want to study but I feel that studying is boring’ were the most central symptoms, while ‘fatigue’ and ‘worthless’ were the bridge symptoms within the burnout-depression-anxiety network. Other central symptoms included ‘worthless’, ‘I can handle my courses’, ‘nervous’, and ‘uncontrollable worry’. Cynicism symptoms ‘I am interested in my major’ and ‘I feel that the knowledge I have learned is useless’ were mostly related to dropout intention. Gender, study phase, and clinical experience didn’t affect the global strength of the burnout-depression-anxiety network. Conclusion: Our results indicated the predominance of cynicism symptoms within the burnout-depression-anxiety network and its substantial impact on dropout intention, suggesting that early detection and intervention for cynicism symptoms in Chinese medical students are in urgent need. Other central and bridge symptoms might also serve as potential targets for the prevention and treatment of burnout, depression, and anxiety among medical students. For example, studies suggest cognitive-behavioral therapy could quickly improve ‘worthless’, which might be beneficial in treating burnout, depression, and anxiety in medical students.

The outbreak of the novel coronavirus disease 2019 (COVID-19) has posed a great impact on people’s mental health, especially for undergraduate students. This study aimed to compare the mental health conditions and academic burnout between medical and non-medical undergraduates in China when the COVID-19 pandemic is mitigating. A cross-sectional online survey was conducted among 4,972 undergraduates between October 2020 and April 2021, when the pandemic was basically under control. The survey included basic demographics information and standardized scales to evaluate depression, anxiety, perceived stress, daytime sleepiness, alcohol abuse/dependence, quality of life, fatigue, and academic burnout. Compared with medical undergraduates, non-medical undergraduates had higher rates of moderate to severe depression symptoms (29.1% vs. 17.9%, P < 0.001), moderate to severe anxiety symptoms (19.7% vs. 8.9%, P < 0.001), alcohol abuse/dependence (16.3% vs.10.3%, P < 0.001), excessive daytime sleepiness (47.4% vs. 43.4%, P = 0.018), high perceived stress (34.7% vs. 22.2%, P < 0.001), high level of fatigue (51.8% vs. 42.2%, P < 0.001), low QOL (35.8% vs. 21.4%, P < 0.001), and higher academic burnout score (59.4 vs. 57.5, P < 0.001). Being non-medical undergraduates, depression, alcohol abuse/dependence, excessive daytime sleepiness, and high perceived stress were positively associated with academic burnout, while high QOL was negatively associated with the burnout (all P < 0.001). Excessive daytime sleepiness was the strongest predictor for academic burnout.