Effects of a Smoke-free Law in Parks and Beaches on Smoking Behaviour: Methods to Determine Effectiveness
Bibliographic record
Abstract
As part of a comprehensive approach to tobacco control,smoke free laws have resulted in reductions of indoor air pollution, improvements in respiratory and cardiovascular health, reduction of smoking uptake by youth, and increasing tobacco use cessation in various jurisdictions. Although many studies have demonstrated the beneficial effects of smoke-free policies in indoor spaces (e.g., restaurants, bars, workplaces, hospital settings, etc.), little is known about the effectiveness of such policies in outdoor public spaces. On September 1st, 2010, Vancouver’s smoke-free by-law for the city’s parks, beaches, and facilities came into effect. The aims of this study are two-fold: a) to examine the effect of this smoke-free law on the frequency of smoking in selected parks and beaches, and b) to determine the change in location of smoking, within parks and beaches, following the enactment of the smoke-free law. The hypotheses guiding this study are: 1) There will be a lower frequency of observed smoking behaviour following the introduction of the law and 2) Smoking behaviour will be dispersed to the peripheries (i.e., margins) of the parks and beaches, following the enactment of the smoke-free law.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".