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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".