Using the intervention ladder to examine policy influencer and general public support for potential tobacco control policies in Alberta and Quebec
Bibliographic record
Abstract
OBJECTIVE: To assess general public and policy influencer support for population-level tobacco control policies in two Canadian provinces. METHODS: We implemented the Chronic Disease Prevention Survey in 2016 to a census sample of policy influencers (n = 302) and a random sample of members of the public (n = 2400) in Alberta and Quebec, Canada. Survey respondents ranked their support for tobacco control policy options using a Likert-style scale, with aggregate responses presented as net favourable percentages. Levels of support were further analyzed by coding each policy option using the Nuffield Council on Bioethics intervention ladder framework, to assess its level of intrusiveness on personal autonomy. RESULTS: Policy influencers and the public considered the vast majority of tobacco control policy options as "extremely" or "very" favourable, although policy influencers in Alberta and Quebec differed on over half the policies, with stronger support in Quebec. Policy influencers and the public strongly supported more intrusive tobacco control policy options, despite anticipated effects on personal autonomy (i.e. for policies targeting children/youth and emerging tobacco products like electronic cigarettes). They indicated less support for fiscally based tobacco control policies (i.e. taxation), despite these policies being highly effective. CONCLUSION: Overall, policy influencers and the general public strongly supported more restrictive tobacco control policies. This study further highlights policies where support among both population groups was unanimous (potential "quick wins" for health advocates). It also highlights areas where additional advocacy work is required to communicate the population-health benefit of tobacco control 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".