Smoking on the margins: a comprehensive analysis of a municipal outdoor smoke-free policy
Bibliographic record
Abstract
BACKGROUND: This study examined the formulation, adoption, and implementation of a ban on smoking in the parks and beaches in Vancouver, Canada. METHODS: Informed by Critical Multiplism, we explored the policy adoption process, support for and compliance with a local bylaw prohibiting smoking in parks and on beaches, experiences with enforcement, and potential health equity issues through a series of qualitative and quantitative studies. RESULTS: Findings suggest that there was unanimous support for the introduction of the bylaw among policy makers, as well as a high degree of positive public support. We observed that smoking initially declined following the ban's implementation, but that smoking practices vary in parks by location. We also found evidence of different levels of enforcement and compliance between settings, and between different populations of park and beach users. CONCLUSIONS: Overall success with the implementation of the bylaw is tempered by potential increases in health inequities because of variable enforcement of the ban; greatest levels of smoking appear to continue to occur in the least advantaged areas of the city. Jurisdictions developing such policies need to consider how to allocate sufficient resources to enhance voluntary compliance and ensure that such bylaws do not contribute to health inequities.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".