E-cigarette use in Canada: prevalence and patterns of use in a regulated market
Bibliographic record
Abstract
OBJECTIVE: Canada is among the few countries in which e-cigarettes containing nicotine are prohibited. To date, there is little evidence on the prevalence and patterns of use of e-cigarettes in markets with product bans. The current study examines e-cigarette use among a sample of non-smokers and smokers in Canada. DESIGN: Online cross-sectional survey. SETTING: Conducted in October 2013 using a commercial panel of Canadians from Global Market Insite, Inc (GMI). PARTICIPANTS: In total, 1095 Canadians were included in the analysis: 311 non-smokers aged 16-24 years (younger non-smokers), 323 smokers aged 16-24 years (younger smokers) and 461 smokers 25 years and older (older smokers). PRIMARY AND SECONDARY OUTCOME MEASURES: E-cigarette ever and current use, types of products used, and reasons for use. RESULTS: Approximately 79% of younger non-smokers, 82% of younger smokers and 81% of older smokers were aware of e-cigarettes. Ever trial of e-cigarettes was reported by 10% of younger non-smokers, 42% of younger smokers and 27% of older smokers. Moreover, current use of an e-cigarette, which was defined as use in the last 30 days, was reported by 0.3% of younger non-smokers, 18% of younger smokers and 10% of older smokers. Among those who had ever tried an e-cigarette, approximately 10% of younger non-smokers, 46% of younger smokers and 43% of older smokers reported trying an e-cigarette that contained nicotine. The most popular e-cigarette flavours were fruit followed by menthol, and the most common reason for using e-cigarettes was to help them quit smoking. CONCLUSIONS: In the context of previous research, it appears that the prevalence of e-cigarette trial has increased in Canada. Although a considerable proportion of non-smokers have tried e-cigarettes, current use is almost entirely concentrated among smokers. Further research should be conducted to monitor e-cigarette use by Canadians.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".