Policy Influencer and General Public Support for Proposed Alcohol Healthy Public Policy Options in Alberta and Quebec, Canada
Bibliographic record
Abstract
OBJECTIVE: Although alcohol consumption is considered a major modifiable risk factor for chronic disease, policies to reduce alcohol-related harm remain low on the Canadian policy agenda. The objective of this study was to understand support for population-level healthy public policies to reduce alcohol-related harm by assessing the attitudes of policy influencers and the public in two Canadian provinces, and by sociodemographic characteristics. METHOD: A stratified sample of the general public (n = 2,400) and a census sample of policy influencers (n = 302) in Alberta and Quebec participated in the 2016 Chronic Disease Prevention Survey, which included questions to assess support for alcohol-specific policies. Differences in levels of support were determined by calculating differences in the proportion of support for alcohol control policies, comparing groups by regional and sociodemographic characteristics. The modified Nuffield Council on Bioethics Intervention Ladder was used to assess support according to the level of individual intrusiveness. RESULTS: We found that policy influencers and general public respondents were supportive of both information-based policies, with the exception of warning labels, and more restrictive policies targeting youth (e.g., enforcement). Both groups were less favorable to alcohol-specific policies that guided choice through disincentives (e.g., taxation). There were more differences in policy support by sociodemographic characteristics among the public. CONCLUSIONS: For health advocates to advance policies to reduce alcohol-related harms at the population level, they will need to mobilize additional support for more intrusive, yet more effective, policy interventions. Advocacy efforts should focus on communicating the effectiveness and positive outcomes of these interventions to help garner support.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".