医学生学业倦怠、抑郁、焦虑关系研究:来自网络分析的证据OACHSSCD

Objective To investigate the relationships between academic burnout,depression,and anxiety among medical students using network analysis,construct a network model,identify core symptoms,and provide a basis for developing intervention measures.Methods A cluster sampling method was used to select 998 students from a medical university as participants.The Maslach Burnout Inventory-Student Survey,the Patient Health Questionnaire-9,and the Generalized Anxiety Disorder-7 were employed to assess their levels of academic burnout,depression,and anxiety,respectively.Network analysis was applied for data processing.The qgraph package in R was used to construct and visualize the regularized partial correlation network for academic burnout,depression,and anxiety.The qgraph and networktools packages were utilized to calculate node centrality metrics,namely expected influence(EI)and bridge centrality metrics,namely bridge expected influence(BEI).The bootnet package was used to evaluate the stability of the network metrics.Results In the academic burnout-depression-anxiety network of medical students,the proportion of true edges was 64.91％(111/171).Strong connections between nodes primarily occurred within the same symptom community(e.g.,"Emotional exhaustion"and"Cynicism","Little interest or pleasure in doing things"and"Feeling down,depressed,or hopeless","Not being able to stop or control worrying"and"Worrying too much about different things").There were relatively strong cross-community connections between"Emotional exhaustion"and"Feeling tired or having little energy","Emotional exhaustion"and"Feeling nervous,anxious,or on edge",and"Trouble falling or staying asleep,or sleeping too much"and"Feeling nervous,anxious,or on edge"."Feeling tired or having little energy","Cynicism",and"Not being able to stop or control worrying"had the highest EI values,respectively."Emotional exhaustion","Feeling bad about yourself—or that you are a failure or have let yourself or your family down",and"Feeling nervous,anxious,or on edge"had the highest BEI values,respectively.Stability analysis indicated that both the network structure and node metrics possessed relatively high stability.Conclusion The nodes within the academic burnout-depression-anxiety network of medical students exhibit extensive and complex interconnections.Targeting high-centrality nodes with interventions combining methods such as cognitive behavioral therapy and emotion regulation strategies may maximize the alleviation of overall academic burnout,depression,and anxiety symptoms.Intervening on high-bridge-centrality nodes may maximally attenuate symptom transmission between different communities(i.e.,different dimensions of psychological issues),thereby effectively controlling symptom co-occurrence.A fine-grained deconstruction of the interrelationships between academic burnout,depression,and anxiety in medical students from a network perspective helps deepen the understanding of their interactions and can provide target references for clinical intervention,ultimately promoting medical students'mental health and learning efficacy.