Examining Engagement With Public Health in the Implementation of School‐Based Health Initiatives: Findings From the <scp>COMPASS</scp> Study
Bibliographic record
Abstract
BACKGROUND: Adolescence coincides with the adoption of health behaviors that schools are not necessarily equipped to properly address. Collaboration between schools and external health services such as public health could fill gaps in addressing student health. METHODS: The current study uses student- and school-level survey data from 59 nonprivate secondary schools in Ontario, Canada in year 6 (2017-2018) of the COMPASS study to examine barriers to improving student health, and analyze the effect of public health engagement on student health behaviors. RESULTS: The majority of schools have received resources from their local public health unit, however, less than one third of schools were developing/implementing programs jointly, and 12% of schools reported no engagement. Students had higher odds of better overall mental health, of meeting screen time guidelines, and were less likely to bully others if public health units solved issues together with schools regarding these health behaviors. CONCLUSIONS: There is a notable lack of consistent public health engagement in schools participating in the COMPASS study despite a need for such services. Creating mechanisms to develop and strengthen effective partnerships between schools and external service providers such as public health might alleviate some barriers to implementing health interventions in Ontario secondary schools.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".