Network analysis of comorbid depression and anxiety and their associations with academic engagement among medical students in the Post-Peak COVID-19 period in China

Medical students frequently face mental health challenges, underscoring the need for prompt identification and intervention. This research is designed to explore the interconnections between depression, anxiety, and academic engagement among medical students in the Post-Peak COVID-19 in China, employing a network analysis approach. In this research, 928 medical students were enrolled. Depression, anxiety, and academic engagement were measured using the nine-item Patient Health Questionnaire, the seven-item Generalized Anxiety Disorder Scale, and the Utrecht Work Engagement Scale for Students, respectively. Central and bridge symptoms were evaluated by the Expected Influence (EI) and bridge EI. The Network Comparison Test was utilized to assess the variability in depression and anxiety symptom associations across gender and residency. In the depression and anxiety network, “Fatigue”, “Guilt”, and “Difficulty relaxing” were the central symptoms. “Sad mood”, “Irritability”, and “Feeling afraid” served as the primary bridge symptoms. “Concentration”, “Anhedonia” and “Motor” exhibited the most robust correlations with academic engagement. Gender and residency did not correlate with global strength and edge weights. The findings showed the complex interplay between depression, anxiety, and academic engagement during the Post-Peak COVID-19 period among Chinese medical students. Future interventions should focus on addressing the central and bridge symptoms within medical students, aiming to improve their mental health outcomes.