Healthy school communities in Canada
Bibliographic record
Abstract
Background and context: Healthy school communities aim to optimise student health and educational achievement. Various models, terms and resources have been used to describe healthy school communities. Policy makers and practitioners have reported confusion around many of the key concepts involved because of the varying models and terms. Importantly, practitioners have reported that the lack of clarity impedes progress related to advancing healthy school work. To address these issues and work towards a common understanding of healthy school communities within the Canadian context, a collaborative process involving practitioners, policy makers and researchers culminated in the production of a concept paper. Objective: Here, we describe the process used to develop the concept paper and summarise what is known about healthy school communities and the effectiveness of the approach. Method: Guided by a steering committee and expert panel, we identified, reviewed and summarised key resources to identify common components and principles necessary for a healthy school communities approach. Results: Core components of healthy school communities that emerged include the presence of education, social and physical environments, policy, community partnerships and the use of evidence. Fundamental principles for creating healthy school communities include the adoption of a whole school approach, education and health service synergy, planning and assessment, leadership and sustainability. Here, we describe the iterative and collaborative process to identify these key components and principles. Conclusion: Beyond the Canadian context, this discussion paper describes a process for enhancing communication among organisations and stakeholders invested in healthy school communities internationally.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.031 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".