Exploring the Primary Mental Health Care Policies for Minors in China from 2015 to 2023 Using a Policy Triangle Framework
Bibliographic record
Abstract
Due to China's rapid economic development in recent years, the prevalence of psychiatric disorders among minors has increased due to intense social competition. China has issued a number of mental health policies to address the issue. Therefore, this project will explore the impact of China's primary mental health care policies on the accessibility of minors to mental health services. This project collected the mental health policy documents for minors published in China from 2015 to 2023. The collected policies are analysed using qualitative research methods. The triangular policy framework is used to analyse the content of policies and actors. Through the thematic analysis of the accessibility related content in the policy content, four main themes of improving service accessibility were obtained: the construction of a mental health service system, the education of mental health knowledge, the construction of a mental health social environment and multi-sectoral cooperation. Thematic analysis of the key actors in the content reveals that the health sector, the education sector, the civil affairs sector and Non-Governmental Organizations have played an important role in the formulation and implementation of the policy. These departments work together to promote the improvement and optimization of minor's mental health services. The project noted that there are still challenges in terms of resource investment, multisectoral collaboration mechanisms and the training and development of professionals. Based on these findings, the project recommends policies to enhance intersectoral collaboration, optimize resource allocation, and train.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".