Comprehensive smoke-free policies attract more support from smokers in Europe than partial policies
Bibliographic record
Abstract
BACKGROUND: Support for smoke-free policies increases over time and particularly after implementation of the policy. In this study we examined whether the comprehensiveness of such policies moderates the effect on support among smokers. METHODS: We analysed two waves (pre- and post-smoke-free legislation) of the International Tobacco Control (ITC) surveys in France, Germany, and the Netherlands, and two pre-legislation waves of the ITC surveys in UK as control. Of 6,903 baseline smokers, 4,945 (71.6%) could be followed up and were included in the analyses. Generalised Estimating Equations (GEE) were used to compare changes in support from pre- to post-legislation to the secular trend in the control country. Multiple logistic regression models were employed to identify predictors of individual change in support. FINDINGS: In France, the comprehensive smoking ban was associated with sharp increases in support for a total smoking ban in drinking establishments and restaurants that were above secular trends. In Germany and the Netherlands, where smoke-free policies and compliance are especially deficient in drinking establishments, only support for a total smoking ban in restaurants increased above the secular trend. Notable prospective predictors of becoming supportive of smoking bans in these countries were higher awareness of cigarette smoke being dangerous to others and weekly visiting of restaurants. CONCLUSIONS: Our findings suggest that smoke-free policies have the potential to improve support once the policy is in place. This effect seems to be most pronounced with comprehensive smoking bans, which thus might be the most valid option for policy-makers despite their potential for creating controversy and resistance in the beginning.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".