Is there an impact of public smoking bans on self-reported smoking status and exposure to secondhand smoke?
Bibliographic record
Abstract
BACKGROUND: Implementation of smoke free policies has potentially substantial effects on health by reducing secondhand smoke exposure. However little is known about whether the introduction of anti-smoking legislation translates into decreased secondhand smoke exposure. We examined whether smoking bans impact rates of secondhand smoke exposure in public places and rates of complete workplace smoking restriction. METHODS: Canadian Community Health Survey was used to obtain secondhand smoking exposure rates in 15 Ontario municipalities. Data analysis included descriptive summaries and 95% confidence intervals were calculated and compared across groups RESULTS: Across all studied municipalities, secondhand smoke exposure in public places decreased by 4.7% and workplace exposure decreased by 2.3% between the 2003 and 2005 survey years. The only jurisdiction to implement a full ban from no previous ban was also the only setting that experienced significant decreases in both individual exposure to secondhand smoke in a public place (-17.3%, 95% CI -22.8, -11.8) and workplace exposure (-18.1%, 95% CI -24.9, -11.3). Exposures in vehicles and homes declined in almost all settings over time. CONCLUSIONS: Implementation of a full smoking ban was associated with the largest decreases in secondhand smoke exposure while partial bans and changes in existing bans had inconsistent effects. In addition to decreasing exposure in public places as would be expected from legislation, bans may have additional benefits by decreasing rates of current smokers and decreasing exposures to secondhand smoke in private settings.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".