Changes in smoking behaviours following a smokefree legislation in parks and on beaches: an observational study
Bibliographic record
Abstract
OBJECTIVE: To examine the effect of an outdoor smokefree law in parks and on beaches on observed smoking in selected venues. METHODS: The study involved repeated observations in selected parks and beaches in Vancouver, British Columbia, Canada. The main outcome measure was changes in observed smoking rates in selected venues from prelaw to 12 months postlaw. RESULTS: No venue was 100% smokefree at the 12-month postlaw observation time point. There was a significant decrease in observed smoking rates in all venues from prelaw to 12-month postlaw (prelaw mean smoking rate=20.5 vs 12-month mean smoking rate=4.7, p=0.04). In stratified analysis by venue, the differences between the prelaw and 12-month smoking rates decreased significantly in parks (prelaw mean smoking rate=37.1 vs 12-month mean smoking rate=6.5, p=0.01) but not in beaches (prelaw mean smoking rate=2.9 vs 12-month mean smoking rate=1.0, p=0.1). CONCLUSIONS: Smokefree policies in outdoor recreational venues have the potential to decrease smoking in these venues. The effectiveness of such policies may differ by the type and usage of the venue; for instance, compliance may be better in venues that are used more often and have enforcement. Future studies may further explore factors that limit and foster the enforcement of such policies in parks and beaches.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| 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".