Exploring the mental health education policies of Chinese college students

Based on policy text analysis and PMC-Index model

Dados Bibliográficos

ID22071498
AutoresErdi Yu, Eunsun Yu (0000-0001-5912-1227, Nanjing University of Posts and Telecommunications), Pu Han, Pin Han (0000-0003-1200-1425, Nanjing University of Posts and Telecommunications, autor correspondente), Xiao Fang (0000-0001-6142-7313, Nanjing University of Posts and Telecommunications)
Ano2025
Volume13
Páginas1560582-1560582
Data de publicação2025-03-21
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoFrontiers in Public Health (JOURNAL)
Identificadores do periódicoISSN: 2296-2565 • E-ISSN: 2296-2565
EditoraFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2025.1560582
PMID40190748
OpenAlexW4408719998
IdiomaEN
Citações recebidas7
Referências citadas57

Introduction: College students' mental health education is crucial for holistic individual development and societal quality. It shapes well-rounded personalities, fosters innovation, and cultivates responsible citizens, contributing to social stability and national development. Our research aims to establish an evaluation system for college student mental health education policies based on the Policy Modeling Consistency Index (PMC-Index) model and text mining techniques, quantitatively assessing 15 representative policies. Method: This paper first retrieves policy documents through specialized policy databases and government websites, excluding policies that have already expired or become invalid. The sample selections of this research range from 2001 to 2024, including national guidelines and specific actions. Referring to the above steps of policy effectiveness evaluation, our research comprehensively analyses the implementation effectiveness of the foregoing policies regarding mental health curricula, service systems, crisis intervention, and family cooperation. In the following, based on 10 major variables, 38 sub-variables are constructed with a binary coding system to quantify the content of policy for objectivity. Then, the ROSTCM 6.0 software is used for text segmentation and word frequency statistics, meanwhile, keywords and semantic networks of core policies will be considered for analysis. Subsequently, the PMC-Index is computed based on the multi-input-output matrix and a diagram of the PMC-Surface is drawn with the help of MATLAB to express policy consistency and deficiencies in different dimensions intuitively. Results: ) reveal deficiencies in policy innovation, long-term planning, and target group coverage. Particularly, short-term policies lack sustainability, limiting their long-term effectiveness in supporting students' mental health. Through PMC-Surface analysis, it is found that lower scores in timeliness and adaptability to target groups are primary factors contributing to surface depressions in the diagram, suggesting that these policies struggle to meet the diverse needs of different types of higher education institutions. The research also highlights significant areas for improvement in resource allocation, support system construction, and personalized mental health services. Some policies fail to address regional disparities, with weaker implementation in underdeveloped areas and insufficient safeguard mechanisms. Discussion: In the future, long-term planning for policy optimization is expected, in which priority would be given to localized resource allocation and close collaboration among schools, families, and society to ensure the comprehensiveness and sustainability of mental health education services. The research has identified certain strengths and weaknesses in the policies concerning mental health education for college students in China, thus providing theoretical references and specific recommendations that can be effectively implemented in higher education institutions

Mental health · Policy analysis · Political science · World Wide Web · Advanced Technologies in Various Fields · Computer Science · Health Policy Implementation Science · Mental Health Treatment and Access · Psychology · Public Administration · Artificial Intelligence