Illicit cigarette purchasing after implementation of menthol cigarette bans in Canada: findings from the 2016–2018 ITC Four Country Smoking and Vaping Surveys
Bibliographic record
Abstract
OBJECTIVE: To examine the impact of menthol cigarette bans on use and purchasing of illicit cigarettes among menthol and non-menthol smokers in seven Canadian provinces. METHODS: Data from 1098 non-menthol smokers and 138 menthol smokers in Canada who completed the ITC Four Country Smoking and Vaping Survey in 2016 (pre-ban) and 2018 (post-ban). Brand validation analysis was conducted to (1) compare self-reported use of menthols versus actual use of menthols as regular brand, and verify self-reported purchasing of menthols among pre-ban menthol smokers at post-ban; and (2) assess pre-post ban changes in purchasing of illicit cigarettes from First Nations reserves among non-menthol smokers and menthol smokers. RESULTS: Among the subset of 138 pre-ban menthol smokers, 36 (19.5%) reported smoking menthols at post-ban. Brand validation analyses showed that 19 (9.0%) were actually using a non-menthol brand; of the 17 (10.5%) who were actually using a menthol brand, 13 (7.9%) bought a menthol brand at last purchase, and 4 (2.6%) bought a non-menthol brand. Among the full sample of smokers who purchased cigarettes from First Nations reserves at both pre-ban and post-ban, there was no change in purchasing of menthols (n=9 menthol smokers; 51.2% vs 51.2%, p=1.00), non-menthols (n=1024 non-menthol smokers; 9.1% vs 8.7%, p=0.69) or all cigarettes (menthol+non-menthol) (n=1086 smokers; 9.7% vs 9.2%, p=0.56). CONCLUSIONS: Actual rates of brand-verified menthol smoking were substantially lower than self-reported rates at post-ban. After Canada's menthol ban, there was no increase in illicit purchasing of menthol or non-menthol cigarettes from First Nations reserves.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".