Support for and reported compliance with smoke-free restaurants and bars by smokers in four countries: findings from the International Tobacco Control (ITC) Four Country Survey
Bibliographic record
Abstract
OBJECTIVE: To explore determinants of support for and reported compliance with smoke-free policies in restaurants and bars across the four countries of the International Tobacco Control (ITC) Four Country Survey. DESIGN: Separate telephone cross-sectional surveys conducted between October and December 2002 with broadly representative samples of over 2000 adult (>or=18 years) cigarette smokers in each of the following four countries: the United States, Canada, the United Kingdom, and Australia. OUTCOME MEASURES: Support for smoke-free policies in restaurants and pubs/bars and reported compliance with existing policies. RESULTS: Reported total bans on indoor smoking in restaurants varied from 62% in Australia to 5% in the UK. Smoking bans in bars were less common, with California in the USA being the only major part of any country with documented bans. Support for bans in both restaurants and bars was related to the existence of bans, beliefs about passive smoking being harmful, lower average cigarette consumption, and older age. Self-reported compliance with a smoking ban was generally high and was associated with greater support for the ban. CONCLUSIONS: Among current cigarette smokers, support for smoking bans was associated with living in a place where the law prohibits smoking. Smokers adjust and both accept and comply with smoke-free laws. Associates of support and compliance are remarkably similar across countries given the notably different levels of smoke-free 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".