Evolution and Devolution of National Physical Activity Policy in Canada
Bibliographic record
Abstract
BACKGROUND: Low levels of physical activity (PA) and fitness have long been a government concern in Canada; however, more than half of adults are inactive. This article examines factors influencing policy development and implementation using Canadian PA policy as a case study. METHODS: Current and historical PA policy documents were amassed from a literature review, audit of government and non government websites and from requests to government officials in each jurisdiction directly responsible for PA. These were analyzed to determine policy content, results, barriers, and success factors. RESULTS: The national focus for PA policy in Canada has devolved to a multilevel system that meets most established criteria for successful strategies. Earlier PA targets have been met; however, the prevalence of PA decreased from 2005 to 2007. Annual per capita savings in health care associated with achieving the earlier target is estimated at $6.15 per capita, yet a fraction of that is directed to promoting PA. CONCLUSION: Evidenced-based strategies that address multiple policy agendas using sector-specific approaches are needed. Sustained high-level commitment is required; advocacy grounded in metrics and science is needed to increase the profile of the issue and increase the commitments to PA policies in Canada and internationally.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".