Intentional, explicit, systematic: Implementation and scale-up of effective practices for supporting student mental well-being in Ontario schools
Bibliographic record
Abstract
Increasingly, the potential for school mental health programming to enhance the well-being of children and youth is being recognized and realized. When evidence-based practices in mental health promotion and prevention are adopted in a whole school manner, students show positive social emotional and academic benefits. These findings have stimulated a proliferation of mental well-being programming for Canadian schools, with variability across offerings in terms of supporting evidence, costs and ease of implementation. In the absence of coordination and guidance, there has been uneven uptake of high-quality programming, resulting in a patchwork of sometimes competing efforts across our country. In order to build cohesive and sustainable evidence-based programming, intentional, explicit and systematic effort must be afforded to matters of implementation and scale-up. In Canada, School Mental Health ASSIST has been developed to provide leadership, implementation support and embeddable resources to the province of Ontario's 72 school districts, and 5000 schools, with a view to ensuring long-term sustainability of best-in-class school mental health practices. Key elements for uptake and scale-up are described, with an implementation science lens and an emphasis on aspects that are generalizable across jurisdictions.
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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 | 0.070 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| 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".