Attitudes to smoke-free outdoor regulations in the USA and Canada: a review of 89 surveys
Bibliographic record
Abstract
OBJECTIVE: To review the published survey data on public support for smoke-free outdoor regulations in the USA and Canada (two countries at the forefront of such policies). DATA SOURCES AND STUDY SELECTION: We searched for English language articles and reports using Medline, Google Scholar and Google for the period to December 2014. We retained population-based surveys of the adult general population in jurisdictions in the USA and Canada, with a minimum survey sample of 500. DATA EXTRACTION: The analysis focused on assessing levels and trends in public support for different types of places and also explored how support varied between population groups. RESULTS: Relevant data were found from 89 cross-sectional surveys between 1993 and 2014. Support for smoke-free regulations in outdoor places tended to be highest for smoke-free school grounds (range: 57-95%) playgrounds (89-91%), and building entrances (45-89%) and lowest for smoke-free outdoor workplaces (12-46%) and sidewalks (31-49%). Support was lower among smokers, though for some types of places there was majority smoker support (eg, school grounds with at least 77% support in US state surveys after 2004). Trend data involving the same questions and the same surveyed populations suggested increased general public and smoker support for smoke-free regulations over time (eg, from 67% to 78% during 2002-2008 for smoke-free school grounds in the USA). Higher support was typically seen from women and some ethnic groups (eg, African-Americans). CONCLUSIONS: Outdoor smoke-free regulations can achieve majority public support, including from smokers.
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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 | 0.021 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.027 | 0.052 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 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".