Fan Wenshu, Zhang Huoyin, Lei Peng, Tang Yue, Du Juan, Li Junyi
Mental Health Education Center for College Students, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China.
School of Psychology, Shenzhen University, Shenzhen, 518060, China.
BMC Psychol. 2025 Apr 28;13(1):448. doi: 10.1186/s40359-025-02731-y.
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.
心理健康问题在中国大学生中普遍存在，症状表现存在性别差异。网络分析提供了一种新方法，用于研究症状之间的复杂相互作用，并识别心理健康问题在结构和动态方面的性别差异。2020年至2023年期间，从中国成都一所大学的18629名新生中收集了心理评估数据。使用高斯图形模型和中心性指标来估计和可视化症状网络。使用R包进行网络比较测试、准确性和稳定性测试以及社区检测，以检验性别差异。心理健康症状网络在不同心理困扰水平上存在差异。在严重困扰组中，男女生的网络在10条边和整体强度上表现出显著差异。自卑、抑郁和焦虑成为核心症状，并通过社区检测得以揭示。单一大学的研究背景可能会限制研究结果对其他人群或文化背景的普遍性。横断面设计排除了对症状关系的因果推断。网络分析为中国大学生心理健康症状的复杂相互作用提供了有价值的见解，突出了严重困扰组中的性别差异。研究结果揭示了核心症状和不同的症状群，强调了制定针对性的个性化干预措施以解决这些特定心理困扰模式的重要性。通过阐明心理健康网络的复杂结构，本研究为在高等教育环境中支持学生福祉的更有效、量身定制的方法奠定了基础。
