Support for banning sale of smoked tobacco products among adults who smoke: findings from the International Tobacco Control Four Country Smoking and Vaping Surveys (2018–2022)
Bibliographic record
Abstract
BACKGROUND: Many people continue to smoke despite strong policies to deter use, thus stronger regulatory measures may be required. In four high-income countries, we examined whether people who smoke would support a total ban on smoked tobacco products under two differing policy scenarios. METHODS: Data were from 14 363 adults (≥18) who smoked cigarettes (≥monthly) and participated in at least one of the 2018, 2020 or 2022 International Tobacco Control Four Country Smoking and Vaping Surveys in Australia, Canada, England and the USA. In 2018, respondents were asked whether they would support a law that totally bans smoked tobacco if the government provides smoking cessation assistance (Cessation Assistance scenario). In 2020 and 2022, respondents were asked a slightly different question as to whether they would support a law that totally bans smoked tobacco if the government encourages people who smoke to use alternative nicotine products like vaping products and nicotine replacement products instead (substitution scenario). Responses (support vs oppose/don't know) were estimated on weighted data. RESULTS: Support was greater for the cessation assistance scenario (2018, 36.6%) than the nicotine substitution scenario (2020, 26.9%; 2022, 26.3%, both p<0.0001). In the longitudinal analysis, there was a significant scenario by country interaction effect with lower support in Canada, the USA and Australia under the substitution scenario than in the cessation scenario, but equivalent levels in England under both scenarios. The strongest correlates of support under both scenarios were planning to quit smoking within 6 months, wanting to quit smoking 'a lot' and recent use of nicotine replacement therapy. CONCLUSIONS: Opposition to banning smoked tobacco predominates among people who smoke, but less with a cessation assistance scenario than one encouraging nicotine substitution. Wanting to quit a lot was the strongest indicator of support.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".