Using a Participatory Approach to the Development of a School‐Based Physical Activity Policy in an Indigenous Community
Bibliographic record
Abstract
BACKGROUND: This study is part of a larger community-based participatory research (CBPR) project to develop, implement, and evaluate the physical activity component of a school-based wellness policy. The policy intervention is being carried out by community stakeholders and academic researchers within the Kahnawake Schools Diabetes Prevention Project, a well-established health promotion organization in the Indigenous community of Kahnawake, Quebec. METHODS: We explored how a group of stakeholders develop a school physical activity policy in a participatory manner, and examined factors serving as facilitators and barriers to the development process. This case study was guided by an interpretive description approach and draws upon data from documentary analysis and participant observation. RESULTS: A CBPR approach allowed academic researchers and community stakeholders to codevelop a physical activity policy that is both evidence-based and contextually appropriate. The development process was influenced by a variety of barriers and facilitators including working within existing structures, securing appropriate stakeholders, and school contextual factors. CONCLUSIONS: This research offers a process framework that others developing school-based wellness policies may use with appropriate modifications based on local environments.
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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 | 0.056 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| 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".