Does the introduction of comprehensive smoke‐free legislation lead to a decrease in population smoking prevalence?
Bibliographic record
Abstract
AIMS: To investigate changes in population smoking prevalence in jurisdictions which have implemented comprehensive smoke-free legislation, taking into account long-term trends in smoking behaviour. DESIGN: Interrupted time series analysis of population-level survey data using segmented regression. SETTING: Twenty-one countries, American states or Canadian provinces which have implemented comprehensive smoke-free legislation. PARTICIPANTS: Respondents sampled in large representative surveys of smoking prevalence. MEASUREMENTS: For each jurisdiction, segmented regression models quantify any upwards or downwards trend in smoking prevalence prior to the introduction of smoke-free legislation, any immediate change in the level of smoking prevalence at the time smoke-free legislation was introduced, and any change in the trend in smoking prevalence post-legislation compared to the pre-legislation period. FINDINGS: In all but three locations there was a statistically significant decline in smoking prevalence prior to the introduction of smoke-free legislation. In two locations, Washington and the Republic of Ireland, there was an immediate decline in the level of smoking prevalence at the introduction of legislation. In six American states there was a significant change in the rate of decline in smoking prevalence, with smoking prevalence declining more steeply in the post-legislation period compared to the pre-legislation period. No change in the level or trend of population smoking prevalence was seen in 13 of the 21 locations studied. CONCLUSIONS: The introduction of comprehensive smoke-free legislation has increased the rate at which smoking prevalence was declining in some locations, but in the majority of jurisdictions had no measureable impact on existing trends in smoking prevalence.
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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 | 0.017 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".