Prioritizing well-being in K-12 education: lessons from a multiple case study of Canadian school districts
Bibliographic record
Abstract
Increasingly, school districts are looking for insights on how to embed a well-being focus across school communities. Well-being in K-12 education is proven to support positive mental health, improve academic performance and contribute to positive outcomes for students and staff. How districts transition to deeply integrate well-being into existing priorities and practices is not well understood. Insights on such shifts can help inform widespread change in education. In 2020, six Canadian school districts participated in case study research to examine how and why districts were able to shift their culture to one that prioritizes well-being. Fifty-five school community members participated in individual semi-structured interviews to explore their perception of well-being in their school communities. Analysis identified six themes: well-being is wholistic and requires balance, student and staff well-being are interconnected, organizational leadership sustains implementation, connection and voice as a catalyst to well-being, building capacity to support well-being action, and charting and re-charting a course. Findings increase our understanding of system-level change, and provide insights to support well-being in education.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.048 | 0.010 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".