The architecture of depression and comorbid symptoms in chinese vocational college students: a dual-method network analysis
摘要
Our study aimed to delineate the architecture of psychopathology in a large, understudied cohort of Chinese vocational college students, identifying common foundations and subgroup-specific symptom pathways.
MethodsWe assessed 9,040 students (65.67% male; Mage = 19.63, SDage = 1.08) from a single institution using the Symptom Checklist-90. Network Analysis (NA) was used to map comorbidity patterns and identify central and bridge symptoms, and Network Comparison Tests (NCT) were performed to examine structural differences across gender and grade levels. Bayesian Network Analysis (BNA) was applied to infer putative directional pathways. Finally, we applied the Hierarchical Influence Model (HIM) to improve the interpretability of the Bayesian network.
ResultsA consistent architecture emerged: depression functioned as the central bridge symptom in the undirected networks and was identified as the primary upstream node in the Bayesian networks across all subgroups. However, downstream pathways diverged significantly. NCT results confirmed statistically significant edge-level differences across gender and grade. For males, depression associated with a cognitive-to-avoidant trajectory; for females, it associated with an affective-to-externalizing pathway. Analysis also revealed distinct patterns across grades. Distinct developmental patterns also emerged: freshmen and juniors showed an affective-to-externalizing pathway consistent with responses to transitional stress, whereas sophomores displayed an affective-to-avoidant pathway linked to social withdrawal.
ConclusionsOur findings reveal a dual structure: a universal foundation centered on depression, branching into gender- and grade-specific pathways. While depression represents a universal focal point for early intervention, the distinct downstream cascades underscore the necessity of tailored mental health strategies for this emerging adult population.