Taking global leadership in banning menthol and other flavours in tobacco: Canada’s experience
Bibliographic record
Abstract
Measures to ban or restrict menthol and other flavours in tobacco products are under consideration or newly implemented in an increasing number of jurisdictions across the world. As one of the world leaders, Canada's experience in successfully developing and implementing such measures can be instructive for other jurisdictions. This paper explores the history of how Canada was able to implement tobacco flavour bans including menthol, examines some of the challenges and presents lessons learnt for other jurisdictions. The crucial motivation for these bans emerged from surveillance data showing high rates of flavoured tobacco use by youth, including menthol cigarette smoking, that was publicised by non-governmental organisations. Further data showed that early legislation in 2009 contained loopholes (cigar size exemptions and menthol exemptions) that limited the benefits of the legislation. Leadership by the provinces created an environment in which the federal ban on menthol ingredients in 2017 was a clear and obvious step to ensure implementation across the country. The Canadian measures have been successful at reducing the use of flavoured tobacco including menthol cigarettes and facilitating smoking cessation. Lessons learnt include the downsides of exemptions, the lack of a contraband issue (despite an existing supply in Canada), the benefits of availability of youth flavour prevalence data and the success of subnational regulations to advance national regulation.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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".