No abstract

A particularly vulnerable yet often-ignored subpopulation is Chinese international students (CIS). Literature suggests CIS suffer from high prevalence of common mental health disorders. Recent advances popularize the use of network analysis on psychopathology data. Our study (N = 421) is the first to investigate depression and anxiety simultaneously from a network perspective in CIS. Results of the Gaussian graphical model (GGM) suggested that: (1) central symptoms identified via the centrality index of strength included depressive symptoms of “anhedonia” and anxiety symptoms of “restlessness” and “tense”; (2) bridge symptoms identified via the bridge expected influence index included depressive symptom of “psychomotor agitation/retardation” and anxiety symptoms of “afraid” and “restlessness”. Results of the Bayesian directed acyclic graph (DAG) demonstrated the predictive priority of depressive symptoms of “anhedonia” and “sadness” in driving comorbidity. The network analyses highlight the node of “anhedonia” (a central node in GGM and the top node in DAG) and several other mostly physical symptoms including “restlessness”, “tense”, “psychomotor agitation/retardation”, and “afraid” as candidates for interventions and show great value in generating clinical insights beyond western sample. Implications and limitations are discussed.