Healthy Public Policy Options to Promote Physical Activity for Chronic Disease Prevention: Understanding Canadian Policy Influencer and General Public Preferences
Bibliographic record
Abstract
BACKGROUND: Attitudes and beliefs of policy influencers and the general public toward physical activity policy may support or impede population-level action, requiring improved understanding of aggregate preferences toward policies that promote physical activity. METHODS: In 2016, the Chronic Disease Prevention Survey was administered to a census sample of policy influencers (n = 302) and a stratified random sample of the public (n = 2400) in Alberta and Québec. Using net favorable percentages and the Nuffield Council on Bioethics' intervention ladder framework to guide analysis, the authors examined support for evidence-based healthy public policies to increase physical activity levels. RESULTS: Less intrusive policy options (ie, policies that are not always the most impactful) tended to have higher levels of support than policies that eliminated choice. However, there was support for certain types of policies affecting influential determinants of physical activity such as the built environment (ie, provided they enabled rather than restricted choice) and school settings (ie, focusing on children and youth). Overall, the general public indicated stronger levels of support for more physical activity policy options than policy influencers. CONCLUSIONS: The authors' findings may be useful for health advocates in identifying support for evidence-based healthy public policies affecting more influential determinants of physical activity.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".