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 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".