Quantitative Research on Mental Health Education Policies in Chinese Universities: An Analysis Based on the PMC Index Model

Rigorous and scientific evaluation methods are crucial for achieving objective and accurate policy assessment results. Conducting an in-depth evaluation of the internal consistency and structural rationality of mental health education policies in Chinese universities is highly significant for exploring how policy evaluation can foster development in this field. This study utilizes the ROST CM6.0 tool to perform text data mining on a sample set of university mental health education policies. Based on the results of high-frequency word analysis and semantic network analysis, it captures the internal logic of policies in this specific domain. The PMC index model is optimized by expanding the primary variables to 10 and refining the secondary variables to 45, thereby constructing a targeted main variable system for policy evaluation in the field of university mental health education. The results indicate that the overall sample of Chinese university mental health education policies demonstrates rationality. However, certain shortcomings exist in areas such as policy instruments, policy targets, service content, and support measures. Accordingly, recommendations are proposed regarding policy coordination, instrument diversity, service precision, and the construction of support systems.