Understanding the impact of the Smoke-Free Ontario Act on hospitality establishments' outdoor environments: a survey of restaurants and bars
Bibliographic record
Abstract
BACKGROUND: The Smoke-Free Ontario Act (SFOA) came into effect in May 2006 and included restrictions to outdoor hospitality areas by only permitting smoking on a patio if the area had no roof. OBJECTIVES: (1) To assess the impact of the SFOA on the prevalence of smoke-free patios in Ontario and (2) to determine the proportion of venues where structural alterations were made rather than going smoke-free in order to achieve compliance with the SFOA. METHODS: A telephone survey of 403 hospitality sector operators/owners in four clustered samples of Ontario, Canada. RESULTS: Based on completed surveys, the SFOA resulted in an increase in prevalence of smoke-free patios, from 5% (n=21) to 25% (n=99). Of the patios where smoking was permitted before the SFOA (n=382), 42% (n=161) had physical structures that would make smoking not permissible under the new act. Operators of half of these venues (n=80) made their patios smoke-free, with most indicating they had no choice given the costs or physical limitations to changing their outdoor environment. The other half (n=81) reported making physical changes, including removing roof structures to achieve compliance. CONCLUSION: The SFOA resulted in greater protection from outdoor secondhand smoke; however, most patios still permitted smoking. Half of the venues that complied with the SFOA by going smoke-free did so involuntarily because of structural and/or financial limitations. The majority of venue operators preferred to permit smoking on their patios, and only made their patios smoke-free when they were required to do so by law.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".