Comparative impact of smoke-free legislation on smoking cessation in three European countries
Bibliographic record
Abstract
BACKGROUND: Little is known about the differential impact of comprehensive and partial smoke-free legislation on smoking cessation. This study aimed to examine the impact of comprehensive smoke-free workplace legislation in Ireland and England, and partial hospitality industry legislation in the Netherlands on quit attempts and quit success. METHODS: Nationally representative samples of 2,219 adult smokers were interviewed in three countries as part of the International Tobacco Control (ITC) Europe Surveys. Quit attempts and quit success were compared between period 1 (in which smoke-free legislation was implemented in Ireland and the Netherlands) and period 2 (in which smoke-free legislation was implemented in England). RESULTS: In Ireland, significantly more smokers attempted to quit smoking in period 1 (50.5%) than in period 2 (36.4%) (p < 0.001). Percentages of quit attempts and quit success did not change significantly between periods in the Netherlands. English smokers were significantly more often successful in their quit attempt in period 2 (47.3%) than in period 1 (26.4%) (p = 0.011). In the first period there were more quit attempts in Ireland than in England and fewer in the Netherlands than in Ireland. Fewer smokers quitted successfully in the second period in both Ireland and the Netherlands than in England. CONCLUSION: The comprehensive smoke-free legislation in Ireland and England may have had positive effects on quit attempts and quit success respectively. The partial smoke-free legislation in the Netherlands probably had no effect on quit attempts or quit success. Therefore, it is recommended that countries implement comprehensive smoke-free legislation.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".