Network analysis of mental health knowledge and stigma among high school students in Sichuan, China

Lack of mental health knowledge (MHK) and stigma toward mental illness pose significant barriers to help-seeking behaviors among Chinese high school students, amid intense academic pressures and cultural influences. Traditional aggregate scoring methods overlook dynamic interconnections between specific knowledge items and stigma attitudes. This study applies network analysis to model MHK and stigma as interconnected systems, identifying central nodes, bridges, and potential intervention targets in a large adolescent sample.

A cross-sectional survey was conducted among 12,537 high school students in Sichuan Province, China, between October 2024 and January 2025.The MHK was assessed using the 20-item Mental Health Knowledge Questionnaire (MHKQ), and the stigma via the 12-item Perceived Devaluation and Discrimination Scale (PDD). Networks were estimated with the IsingFit algorithm in R (v4.3.2), incorporating partial correlations. Centrality (strength), bridge expected influence, and stability were computed. The NodeIdentifyR algorithm (NIRA) was used to simulated aggravating and alleviating interventions on network sum scores. Gender invariance was tested using the NetworkComparisonTest package.

The network revealed two communities (MHK and stigma) with dense intra-cluster connections and key bridges. MHKQ11 ("optimistic attitude, good relationships, and healthy habits help maintain mental health") showed the highest centrality (strength: 12.06), serving as a MHK hub. MHKQ10 ("short-term medication suffices for severe mental illnesses like schizophrenia without long-term adherence") bridged to stigma items (e.g., Stigma5: "most employers will not hire a person who has been hospitalized for mental illness"; bridge expected influence: 0.957). Network stability was robust (CS > 0.672). Aggravating simulations were associated with the highest sum scores for MHKQ10, MHKQ13, and MHKQ14. Alleviating interventions showed the greatest potential for score reduction via MHKQ11, MHKQ8, and MHKQ16. Gender networks showed invariance (global strength difference: 0.37, p = 0.693).

This network analysis highlights MHKQ10 and MHKQ11 as pivotal targets for stigma reduction, with misconceptions about treatment adherence linking knowledge deficits to devaluation perceptions. Gender-invariant structures suggest universal applicability for school-based interventions, aligning with China's mental health initiatives to enhance literacy and promote equity.