Social determinants of support for tobacco control policies to end the use of commercial cigarettes: findings from the 2022 International Tobacco Control Four Country Smoking and Vaping Survey
Bibliographic record
Abstract
BACKGROUND: Some countries are exploring policies that might help lower cigarette smoking prevalence to below 5% by 2030. This study assesses support for three policies among adults who smoke: (1) ending smoked tobacco sales within 10 years; (2) limiting cigarette nicotine content to make them less addictive; and (3) restricting smoked tobacco sales for life to anyone currently under 18 (Smoke-Free Generation, SFG). METHODS: Data were from the 2022 International Tobacco Control Four Country Smoking and Vaping Survey, including 2723 adults who smoked at least monthly in Australia, Canada, England and the USA. We estimated the weighted prevalence of support and used logistic regression to assess whether support (vs no support) differed by sociodemographic variables (country, gender, age, race/ethnicity, education, income). RESULTS: Support was 27.1% for ending smoked tobacco sales within 10 years, 53.5% for limiting the nicotine content of cigarettes and 51.7% for an SFG law. US adults had consistently lower odds of support compared with those in Canada and England (all policies: p<0.01). Women and white individuals had lower odds of supporting an end to smoked tobacco sales (adjusted OR (aOR): 0.63, p=0.02; 0.68, p=0.03) and an SFG law (aOR: 0.72, p=0.04; 0.66, p=0.01). Women (aOR: 1.41, p<0.01) were more supportive of nicotine reduction. Individuals with moderate (aOR vs high: 1.34, p=0.03) and low (aOR: 1.41, p=0.03) education, and those with financial difficulties (aOR: 1.36, p=0.04) showed higher support for an SFG law. No differences in support were observed by age in the pooled data. CONCLUSIONS: Support for policies to reduce the use of commercial cigarettes was moderate-to-high among adults who smoke, although it varied significantly by country, gender and income. Tailored communication strategies could help increase support and promote policy adoption.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".