Assessing general public and policy influencer support for healthy public policies to promote healthy eating at the population level in two Canadian provinces
Bibliographic record
Abstract
OBJECTIVE: To assess and compare the favourability of healthy public policy options to promote healthy eating from the perspective of members of the general public and policy influencers in two Canadian provinces. DESIGN: The Chronic Disease Prevention Survey, administered in 2016, required participants to rank their level of support for different evidence-based policy options to promote healthy eating at the population level. Pearson's χ 2 significance testing was used to compare support between groups for each policy option and results were interpreted using the Nuffield Council on Bioethics' intervention ladder framework. SETTING: Alberta and Québec, Canada.ParticipantsMembers of the general public (n 2400) and policy influencers (n 302) in Alberta and Québec. RESULTS: General public and policy influencer survey respondents were more supportive of healthy eating policies if they were less intrusive on individual autonomy. However, in comparing levels of support between groups, we found policy influencers indicated significantly stronger support overall for healthy eating policy options. We also found that policy influencers in Québec tended to show more support for more restrictive policy options than their counterparts from Alberta. CONCLUSIONS: These results suggest that additional knowledge brokering may be required to increase support for more intrusive yet impactful evidence-based policy interventions; and that the overall lower levels of support among members of the public may impede policy influencers from taking action on policies to promote healthy eating.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".