MétaCan
Menu
Back to cohort
Record W4408333220 · doi:10.1177/1179173x251322597

Canadian Youth Preferences for E-Cigarettes: A Discrete Choice Experiment

2025· article· en· W4408333220 on OpenAlexaffabout
Daniel Eisenkraft Klein, Jiamin Shi, Robert Schwartz

Bibliographic record

VenueTobacco Use Insights · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsNoveltyDiscrete choiceNicotineBusinessPublic economicsAdvertisingMarketingPsychologyEnvironmental healthEconomicsSocial psychologyMedicineEconometrics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.122
GPT teacher head0.243
Teacher spread0.121 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueTobacco Use InsightsSame topicEconomic and Environmental ValuationFrench-language works237,207