Smoking and vaping among Canadian youth and adults in 2017 and 2019
Bibliographic record
Abstract
E-cigarettes remain a contentious topic in public health, with debates focussing on their benefits as a smoking cessation aid1–3 versus potential increases in nicotine use among non-smoking young people.4 Accordingly, the public health impact of e-cigarettes will be determined by who is using them and for what purpose. To date, most studies exploring the prevalence of vaping have been conducted among either youth5–10 or adults,11–13 with little evidence on overall populations of vapers. Specifically, evidence is lacking regarding the relative contribution of youth and adults, smokers and never smokers, and how these groups have contributed to overall increases in vaping at the population level. Evidence is also required to evaluate the impact of e-cigarette policies on patterns of vaping among these different groups. Canada represents an interesting case study given recent shifts in the regulatory framework for e-cigarettes.14 Prior to May 2018, e-cigarettes containing nicotine could not be sold or marketed without approval; although no products were approved for legal sale, they were widely available.15 In May 2018, the Tobacco and Vaping Products Act (TVPA) permitted the sale of nicotine-containing e-cigarettes, as well as wider advertising and promotion of e-cigarettes, which increased retail accessibility and the presence of international brands.14 Studies have highlighted increases in youth vaping following implementation of the TVPA,5 6 although there are few estimates on changes in vaping at the population level in Canada. This study uses data from nationally representative surveys to examine how smoking and vaping evolved at the population level in Canada following the implementation of the TVPA. Data are from the 2019 Canadian Tobacco and Nicotine Survey (CTNS),16 a national monitoring survey in Canada. Briefly, the CTNS is a probability-based sample of the general population of Canada aged 15 years or older (n=8600) …
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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 | 0.000 | 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".