Understanding key influencers' attitudes and beliefs about healthy public policy change for obesity prevention
Bibliographic record
Abstract
OBJECTIVE: As overweight and obesity is a risk factor for chronic diseases, the development of environmental and healthy public policy interventions across multiple sectors has been identified as a key strategy to address this issue. METHODS: In 2009, a survey was developed to assess the attitudes and beliefs regarding health promotion principles, and the priority and acceptability of policy actions to prevent obesity and chronic diseases, among key policy influencers in Alberta and Manitoba, Canada. Surveys were mailed to 1,765 key influencers from five settings: provincial government, municipal government, school boards, print media companies, and workplaces with greater than 500 employees. A total of 236 surveys were completed with a response rate of 15.0%. RESULTS: Findings indicate nearly unanimous influencer support for individual-focused policy approaches and high support for some environmental policies. Restrictive environmental and economic policies received weakest support. Obesity was comparable to smoking with respect to perceptions as a societal responsibility versus a personal responsibility, boding well for the potential of environmental policy interventions for obesity prevention. CONCLUSIONS: This level of influencer support provides a platform for more evidence to be brokered to policy influencers about the effectiveness of environmental policy approaches to obesity prevention.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".