Characteristics of e-cigarette users and their perceptions of the benefits, harms and risks of e-cigarette use: Survey results from a convenience sample in Ottawa, Canada
Bibliographic record
Abstract
Introduction Although e-cigarette use (vaping) is increasing in Canada, few attempts have been made to describe e-cigarette users (vapers). In this context, we conducted a study in Ottawa, Canada, to describe e-cigarette users' perceptions of the benefits, harms and risks of e-cigarettes. We also collected information on why, how and where they use e-cigarettes as well as information on side effects. Methods A 24-item online survey was administered to individuals who purchased e-cigarettes or e-cigarette-related supplies at one of Ottawa's 17 e-cigarette shops. Descriptive analyses characterized respondents, and logistic regression models were fitted to evaluate the relationship between respondents' characteristics and their perception of e-cigarette harms. Results The mean age of the 242 respondents was 38.1 years (range: 16-70 years); 66% were male. Nearly all had smoked 100 or more cigarettes in their lifetime (97.9%). More than 80% indicated that quitting smoking was a very important reason for starting to use e-cigarettes and 60% indicated that they intend to stop using e-cigarettes at some point. About 40% reported experiencing some side effects within 2 hours of using e-cigarettes. Those who did not report experiencing any of the listed side effects had approximately 3.2 times higher odds of perceiving e-cigarettes as harmless than those who reported having side effects (odds ratio = 3.17; 95% confidence interval: 1.75-5.73). Conclusion Our findings suggest that most e-cigarette users are using them to reduce or stop smoking cigarettes and perceive them as harmless. Due to our use of convenience sampling, the reader should be cautious in generalizing our findings to all Canadian e-cigarette users.
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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 | 0.001 |
| 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".