Exposure to smoking on patios and quitting: a population representative longitudinal cohort study
Bibliographic record
Abstract
OBJECTIVES: Smoke-free policies not only reduce harm to non-smokers, they may also reduce harm to smokers by decreasing the number of cigarettes smoked and increasing the likelihood of a successful quit attempt. However, little is known about the impact of exposure to smoking on patios on smoking behaviour. DESIGN AND PARTICIPANTS: Smokers from the Ontario Tobacco Survey, a longitudinal population representative cohort of smokers (2005-2011). There were 3460 current smokers who had completed one to six follow-ups and were asked at each follow-up whether or not they had been exposed to smoking on patios in the month. MAIN OUTCOME MEASURES: Generalised estimating equations and survival analysis were used to examine the association between exposure to patio smoking and smoking behaviour changes (making a quit attempt and time to relapse after a quit attempt), controlling for potential confounders. RESULTS: Smokers who were exposed to smoking on patios (adjusted incident rate ratio (aIRR) = 0.89; 95% CI 0.81 to 0.97) or had been to a patio (aIRR = 0.86; 95% CI 0.74 to 0.99) were less likely to have made a quit attempt than smokers who had not visited a patio. Smokers who were exposed to smoking on patios were more likely to relapse (adjusted HR=2.40; 95% CI 1.07 to 5.40)) after making a quit attempt than those who visited a patio but were not exposed to smoking. CONCLUSIONS: Exposure to smoking on patios of a bar or restaurant is associated with a lower likelihood of success in a quit attempt. Instituting smoke-free patio regulations may help smokers avoid relapse after quitting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".