It takes a community: exploring APPLE Schools’ impact within and beyond school walls in Canada
Bibliographic record
Abstract
Childhood is a critical stage to develop healthy behaviours, and the school years are an important developmental period in which children are learning and growing. School-based health promotion interventions are increasingly using the comprehensive school health (CSH) approach that has been found effective at improving health behaviours, while adapting to the school and community context to create and sustain a healthy school culture. The interconnected environments of school, home and community are key to the CSH approach, however, there is minimal research that has been conducted to determine the impact at the community-level. This research used a qualitative multiple case study design to explore the influence of a school-based health promotion intervention, APPLE Schools, on the school and community environments from the perspectives of school staff in Alberta, Canada. Twenty-two participants participated in walking interviews in five schools. The walking interview approach combined interviewing and field observation and empowered participants to share their local expertise and knowledge within the context of school and community environments. Interviews were transcribed and analysed using an inductive thematic approach. Four themes were identified: APPLE schools look different, APPLE Schools build champions, APPLE Schools builds community partnerships and APPLE Schools honours community culture. Health promotion practice in schools was uniquely adapted based on strengths and needs of each school and was broadly influenced by the community outside of school walls. In sum, it was found that school-based health promotion reaches beyond school walls and can influence the broader community environment.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.025 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".