School Health Education Curricula in Canada: A Critical Analysis
Bibliographic record
Abstract
Little is known about school health education curricula across Canada, and minimal literature has critically analyzed these curricula for further enhancement. In this study the authors examined health education curricula in Canada in all jurisdictions nationwide. As a result, the study delineates the profiles of health education curricula across Canada, discusses the core content and frameworks in these curricula, and reveals common trends against the scholarship in the field of school health education. The findings presented here will provide scholars and professionals with a comprehensive overview of school-based health education content, and will inform future curriculum revisions and improvements of overall school health education in Canada and across the globe. Les programmes-cadres d’éducation à la santé offerts dans les écoles canadiennes sont peu connus et peu d’études les ont analysés pour éventuellement contribuer à les améliorer. La recherche qui suit présente une analyse de ces programmes-cadres d’éducation à la santé enseignés dans toutes les régions du Canada. La recherche trace le profil des programmes d’éducation à la santé offerts, discute de leurs grandes composantes et de leur cadre de référence et fait ressortir des tendances communes au niveau des contenus. En plus de donner aux universitaires et aux professionnels un aperçu détaillé et complet des cours d’éducation à la santé offerts dans les écoles, les résultats de l’étude pourraient apporter une contribution à la révision et à l’amélioration de ces programmes dans le futur, au Canada et ailleurs dans le monde.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.028 | 0.037 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".