“It trickles into the community”: A case study of the transfer of health promoting practices from school to community in Canada
Bibliographic record
Abstract
The school is an ideal setting to promote children's health and is an equitable way to reach children in early developmental years. Healthy children are stronger learners and wholistic health approaches taken in schools can help children thrive. Interventions in the school are strengthened when school, home, and community work together, yet many interventions have not reported the school and community connection and influence. The purpose of this study was to determine if and how the intervention, APPLE Schools, has impacted the community environment. One community in Alberta, Canada was chosen as a case study. An instrumental case study approach was taken, and data generation was guided by focused ethnography. Data were generated through community partner interviews (n = 17) and document analysis. Reflexive thematic analysis was used to analyze data. Results were represented by three main themes and demonstrated APPLE Schools created impact beyond the school setting through a stepped approach: 1) Foundation: establishes a healthy school culture; 2) Action: tying the work of schools and communities together; and 3) Impact: changes in school practices ripple out to promote health in the community. This research provides compelling evidence that comprehensive school health approaches can impact community environments outside the school and result in stronger health promoting practices both within and outside the school.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.047 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.005 |
| 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".