Bibliographic record
Abstract
Objective: Guidelines from a variety of jurisdictions for the health-promoting schools (HPS) approach include healthy school policy as a critical element. Research also supports the importance of policy; however, there seems to be a lack of information on how to develop and implement policy. The article examines the processes involved in one school division’s development and implementation of healthy school policy. Design: The study emerged from the Battle River Project, a multilevel partnership designed to explore the efficacy of implementing HPS at a school division level. The project intervention involved support for schools, a division-level steering committee, and a framework for implementation. Setting: The Battle River School Division is located in Alberta, Canada, and serves both rural and urban school communities. The study involved 21 of the 36 schools in the division and was initiated by the Ever Active Schools programme. Method: Development and implementation of policy and procedure were examined using case study methodology. Data gathered included interviews, focus groups, documents and observation. Results: Four primary themes were revealed through data analysis. Perceptions and misconceptions; bottom–up/top–down; flexible rigidity; and the way we do business. Conclusion: The process of developing and implementing healthy school policy can be streamlined by planning for clear communication, involving all stakeholders, and by embedding health into the structures of a school jurisdiction.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.281 | 0.180 |
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".