Canadian Youth Preferences for E-Cigarettes: A Discrete Choice Experiment
Bibliographic record
Abstract
Objectives: The novelty of e-cigarette regulatory policy poses difficulties for evidence-informed decision making because there is little evaluative evidence on the effects of specific policies. One way to provide evidence to inform Canadian policy in this situation is to learn from users how they would behave under different policy scenarios without actually implementing those policies in real-world settings. Discrete Choice Experiments provide an opportunity to undertake this research. Methods: We recruited an online sample of 600 e-cigarette current and past users aged 16-25, using an existing panel of recently recruited e-cigarette users, to participate in a discrete choice experiment. Participants chose their preferred option from a choice of 2 e-cigarette products described by 4 attributes: flavour availability, location availability, nicotine concentration, and price. Results: Our findings provide an overview of how important each attribute (price, nicotine concentration, availability, and flavour) is to young e-cigarette users. Across all features, as price increases, respondents were less willing to purchase. The study provides evidence that while all 4 attributes have strong effects, nicotine concentration and flavour most significantly influenced preferences for e-cigarettes. Conclusion: This could provide points of comparison and a better understanding of how hypothetical regulatory restrictions could prevent youth uptake of e-cigarettes, encourage current youth vapers to quit vaping, and make e-cigarettes available and useful for smokers interested in vaping to help them completely quit combustible cigarette smoking.
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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".