Application of the “hospital-school-home-community” integrated mental health service model for primary and secondary school students in Chongqing, China
Bibliographic record
Abstract
Background: Childhood and adolescence are critical periods for mental health development, with a significant risk for mental disorders. The Chinese government has prioritized student health and well-being, encouraging policies to support youth mental health. However, challenges remain due to high technical demands and inter-departmental coordination. Methods: The "pathway through care" model connects hospitals, schools, communities, and families to collaboratively promote adolescent mental health. The model employs general screening, interviews, and intervention strategies at various levels, including mental health promotion and training for students, educators, and parents. The model's operation is led by a tertiary care team in collaboration with school districts. Results: Since the implementation of the model in Chongqing, a large-scale screening of students has been conducted, with over 330,000 students screened and a significant number receiving face-to-face interviews with psychiatrists/psychologists. The model has facilitated the early identification of students at risk, appropriate referrals for care, and ongoing support within the school setting. It has also engaged parents and the wider community in addressing youth mental health needs. Conclusions: The "Hospital-School-Home-Community" integrated mental health service model has demonstrated success in improving child and youth mental health within the school setting in Chongqing. It has effectively linked education and health systems to address a full continuum of care.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".