Utility of Surveillance Research to Inform Physical Activity Policy: An Exemplar From Canada
Bibliographic record
Abstract
BACKGROUND: There are several well-known risk factor monitoring systems, but few examples of comprehensive surveillance systems designed specifically to inform physical activity (PA) policy. This paper examines the utility of Canada`s Physical Activity and Sport Monitoring System in guiding policy and practice. METHODS: Indicators were determined in conjunction with government, nongovernmental associations and academics. Serial measures were collected from representative population (telephone interviews, n = 4000 to 11,000) and setting-based (postal surveys, n = 1425 to 4304) surveys. RESULTS: Adult PA was higher in 2014 (47%) than 1998 (37%). The prevalence of knowledge about sufficient PA to meet national guidelines increased (31% to 57%). Most adults (66%) reported having many safe places to walk locally. Having policies to encourage walking and cycling when redeveloping communities increased by community size (5% to 37%). PA promotion was available in 10% to 15% of workplaces. Most parents (64%) provided transportation to support their child's PA. The prevalence of policies mandating daily PE increased 2001 to 2011 (36% to 55%), as did having no policy to hire qualified PE teachers (25% to 34%). CONCLUSIONS: Canada's surveillance system has provided information for guiding policy planning, resource allocation, setting and tracking national goals, assessing changes in PA determinants, and evaluating national campaigns, naturally occurring experiments, and innovative policies.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".