Was it diffusion? Exploring the spread of daily physical activity policies in Canada
Bibliographic record
Abstract
Background: Between 2005 and 2010, five Canadian provinces adopted daily physical activity (DPA) policies. This study investigated the adoption and spread of those DPA policies in the 5-year period. Purpose: The purpose of this study was to investigate the role, if any, of diffusion in the adoption and spread of DPA policies across provinces in Canada over a 5-year period. Methods: Semi-structured interviews were conducted with 15 DPA policy influencers. Transcripts were analyzed using directed content analysis to examine alignment with an established diffusion framework. Findings were also examined for consistency with mechanisms of policy diffusion and alternative explanations of policy spread. Results: Participant responses aligned most closely with diffusion framework components of attributes of the innovation, system antecedents for innovation, implementation and routinization, receptive context for change, assimilation by the system, system readiness for innovation, interorganizational networks and collaboration, and communication and influence. Findings also revealed evidence of policy learning, imitation, and competition across jurisdictions as the dominant mechanisms of policy diffusion. There was limited evidence that common shock and independent causation contributed to policy spread. Conclusions: The spread of DPA policies across Canada between 2005 and 2010 was consistent with theoretical concepts and mechanisms of policy diffusion.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".