Exploring Consumer Preferences for Cannabis Vaping Products to Support Public Health Policy: A Discrete Choice Experiment
Bibliographic record
Abstract
BACKGROUND: Phase two of cannabis legalisation in Canada brought cannabis vaping products to the market. This decision was controversial due to an outbreak of vaping cannabis use-associated lung injury. This resulted in three provinces banning the sale of cannabis vaping products causing inequitable access. This study sought to explore consumer preferences for cannabis vaping products to inform cannabis policy. METHODS: We used a discrete choice experiment to explore consumer preferences for attributes of cannabis vaping products. Attributes included type of device, price, tetrahydrocannabinol (THC) potency, vape liquid content, product recommendations and Health Canada regulation. Participants lived in Canada, were aged ≥ 19 years, and had purchased a cannabis vape in the last 12 months. A multinomial logit (MNL) model was used for the base model, and latent class analysis to assess preference sub-groups. RESULTS: In total, 384 participants completed the survey; the MNL model showed that price and potency were the most important attributes. A three-group latent class model showed that ~ 40% of the sample was driven primarily by Health Canada Regulation and were willing to pay $56 more for a product that was regulated compared to one that was not. About 33% of the sample was driven by price, and 26% was driven by type of device. CONCLUSION: While regulated status by Health Canada was most important to some consumers (~ 40%), nearly 60% of the sample were willing to make trade-offs in regulated status for products with a lower price. Therefore, policymakers need to consider the broader public health implications of banning cannabis vapes in some regions.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".