Uncovering the complex interactions of mental health symptoms in Chinese college students: insights from network analysis.

Mental health problems are prevalent among Chinese college students, with gender differences in symptom presentation. Network analysis provides a novel approach to investigate the complex interactions between symptoms and identify gender differences in the structure and dynamics of mental health problems. Psychological assessment data were collected from 18,629 freshmen at a university in Chengdu, China, between 2020 and 2023. Gaussian Graphical Models and centrality indices were used to estimate and visualize symptom networks. Network comparison tests, accuracy and stability tests, and community detection were performed using R packages to examine gender differences. Mental health symptom networks differed across psychological distress levels. In the severe distress group, male and female students' networks exhibited significant differences in 10 edges and overall strength. Inferiority, depression, and anxiety emerged as central symptoms, and revealed by community detection. The single-university setting may limit the generalizability of the findings to other populations or cultural contexts. The cross-sectional design precludes causal inferences about symptom relationships. Network analysis offers valuable insights into the complex interactions of mental health symptoms among Chinese college students, highlighting gender differences in the severe distress group. The findings reveal central symptoms and distinct symptom clusters, underscoring the importance of developing targeted, personalized interventions that address these specific patterns of psychological distress. By illuminating the intricate structure of mental health networks, this research provides a foundation for more effective, tailored approaches to support student well-being in higher education settings.