An evaluation of the ‘bottom-up’ implementation of the <i>Active at school!</i> programme in Quebec, Canada
Bibliographic record
Abstract
The lack of physical activity (PA) amongst children is a public health concern in many industrialized countries. School-based daily physical activity (DPA) policies are a promising intervention for increasing PA levels amongst children. Informed by a logic model framework, this study examines the factors associated with meeting a 'top-down' DPA objective in the context of a 'bottom-up' implementation of a school-based DPA initiative in Quebec, Canada. An online survey assessing school-level inputs, outputs and outcomes was sent to all participating schools (415). Crude odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using logistic regression to evaluate potential associations between factors (inputs and outputs) and the school's adherence to providing at least 60 minutes of DPA (outcome). Adjusted ORs (AORs) and 95% CIs were calculated using a multivariate logistic regression to identify the best set of factors to predict adherence to the DPA objective. A total of 404 schools completed the questionnaire, amongst which 71% reported meeting the DPA target by implementing school-tailored activities. Three factors were identified as the best set of school inputs and outputs to predict meeting the objective: financial resources (per student) (AOR = 1.02; 95% CI 1.01-1.03), a shared vision amongst the school-team members that PA benefits learning outcomes (AOR = 1.94; 95% CI 1.04-3.19) and having conducted a detailed situational analysis (AOR = 1.89; 95% CI 1.00-3.58). Given that 'bottom-up' implementation might favour the development of policies that are more acceptable to stakeholders, our results should be considered by decision-makers and school administrators when implementing DPA initiatives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".