Changes in adults’ vaping and smoking behaviours associated with aerosol-free laws
Bibliographic record
Abstract
OBJECTIVES: Banning e-cigarette use in public places has attracted considerable debate, with governments adopting different policies. However, little is known about the outcomes of such bans. We investigated the association of banning e-cigarette use in public places and workplaces in Canadian provinces with adults' vaping and smoking behaviours. DESIGN: Difference-in-differences. SETTING: Nationally representative Canadian Tobacco Use Monitoring Survey (CTUMS) and Canadian Tobacco, Alcohol and Drugs Survey (CTADS). PARTICIPANTS: Adults aged 19 and older from CTADS 2013-2017 for e-cigarette outcomes (N=36 562) and from CTUMS/CTADS 2004-2017 for combustible cigarette outcomes (N=178 654). INTERVENTIONS: Bans on e-cigarette use in public places and workplaces in Canadian provinces. MAIN OUTCOME MEASURES: Past 30-day e-cigarette use, current combustible cigarette use, use of e-cigarettes when unable to smoke combustible cigarettes. RESULTS: After the bans, e-cigarette use in the past 30 days did not change significantly in provinces with a ban compared with provinces without a ban (0.004; 95% CI -0.025 to 0.032; p=0.783). The bans also had no impact on current combustible cigarette use (0.009; 95% CI -0.019 to 0.037; p=0.488). There is evidence of ban evasion among young people aged 19-24 who, after the bans, reported higher use of e-cigarettes when unable to smoke combustible cigarettes (0.114; 95% CI -0.023 to 0.250; p=0.092). CONCLUSIONS: Two years after implementation, the aerosol-free laws in Canada had no impacton adults' vaping and smoking behaviours. Policy efforts are urgently needed to improve the ban enforcement and to deal with discreet vaping among young adults.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".