A multiple case history and systematic review of adoption, diffusion, implementation and impact of provincial daily physical activity policies in Canadian schools
Bibliographic record
Abstract
BACKGROUND: Few children meet physical activity (PA) recommendations, and are therefore at increased risk for overweight/obesity and adverse health outcomes. To increase children's opportunities for PA, several Canadian provinces have adopted school-based daily PA (DPA) policies. It is not clear why some jurisdictions have adopted DPA policies, and others have not, nor whether these policies have been implemented and have achieved their intended outcomes. The purpose of this study was to understand the processes underlying adoption and diffusion of Canadian DPA policies, and to review evidence regarding their implementation and impact. METHODS: We adopted a multiple case history methodology in which we traced the chronological trajectory of DPA policies among Canadian provinces by compiling timelines detailing key historical events that preceded policy adoption. Publicly available documents posted on the internet were reviewed to characterize adopter innovativeness, describe the content of their DPA policies, and explore the context surrounding policy adoption. Diffusion of Innovations theory provided a conceptual framework for the analyses. A systematic literature search identified studies that had investigated adoption, diffusion, implementation or impact of Canadian DPA policies. RESULTS: Five of Canada's 13 provinces and territories (38.5%) have DPA policies. Although the underlying objectives of the policies are similar, there are clear differences among them and in their various policy trajectories. Adoption and diffusion of DPA policies were structured by the characteristics and capacities of adopters, the nature of their policies, and contextual factors. Limited data suggests implementation of DPA policies was moderate but inconsistent and that Canadian DPA policies have had little to no impact on school-aged children's PA levels or BMI. CONCLUSIONS: This study detailed the history and current status of Canadian DPA policies, highlighting the conditional nature of policy adoption and diffusion, and describing policy and adopter characteristics and political contexts that shaped policy trajectories. An understanding of the conditions associated with successful policy adoption and diffusion can help identify receptive contexts in which to pioneer novel legislative initiatives to increase PA among children. By reviewing evidence regarding policy implementation and impact, this study can also inform amendments to existing, and development of future PA policies.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".