Socioeconomic Variation in the Prevalence, Introduction, Retention, and Removal of Smoke-Free Policies among Smokers: Findings from the International Tobacco Control (ITC) Four Country Survey
Bibliographic record
Abstract
INTRODUCTION: Exposure to secondhand smoke causes premature death and disease in non-smokers and indoor smoke-free policies have become increasingly prevalent worldwide. Although socioeconomic disparities have been documented in tobacco use and cessation, the association between socioeconomic status (SES) and smoke-free policies is less well studied. METHODS: Data were obtained from the 2006 and 2007 Waves of the International Tobacco Control Four Country Survey (ITC-4), a prospective study of nationally representative samples of smokers in Canada, the United States, the United Kingdom, and Australia. Telephone interviews were administered to 8,245 current and former adult smokers from October 2006 to February 2007. Between September 2007 and February 2008, 5,866 respondents were re-interviewed. Self-reported education and annual household income were used to create SES tertiles. Outcomes included the presence, introduction, and removal of smoke-free policies in homes, worksites, bars, and restaurants. RESULTS: Smokers with high SES had increased odds of both having [OR: 1.54, 95% CI: 1.27-2.87] and introducing [OR: 1.49, 95% CI: 1.04-2.13] a total ban on smoking in the home compared to low SES smokers. Continuing smokers with high SES also had decreased odds of removing a total ban [OR: 0.44, 95% CI: 0.26-0.73]. No consistent association was observed between SES and the presence or introduction of bans in worksites, bars, or restaurants. CONCLUSIONS: The presence, introduction, and retention of smoke-free homes increases with increasing SES, but no consistent socioeconomic variation exists in the presence or introduction of total smoking bans in worksites, bars, or restaurants. Opportunities exist to reduce SES disparities in smoke-free homes, while the lack of socioeconomic differences in public workplace, bar, and restaurant smoke-free policies suggest these measures are now equitably distributed in these four countries.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".