Exploring consumer preferences for cannabis edible products to support public health policy: A discrete choice experiment
Bibliographic record
Abstract
Abstract Background In October 2019, cannabis edibles were legalized for sale in Canada. This move was intended to improve public safety by regulating contents (including a maximum 10 mg tetrahydrocannabinol (THC) per package) and packaging to prevent accidental ingestion or over consumption. This study aimed to explore consumer preferences for cannabis edibles to inform cannabis policy. Methods We explored the relative importance and trade-offs consumers make for attributes of cannabis edibles using a discrete choice experiment. Attributes included type of edible, price, THC content, cannabis taste, package information, product consistency, product recommendations, and Health Canada regulation. Participants lived in Canada, were 19 years of age or older, and purchased a cannabis edible 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 Among 684 participants, the MNL model showed that potency was the most relevant followed by edible type. A two-group latent class model revealed two very distinct preference patterns. Preferences for group 1 (∼65% of sample) were driven primarily by edible type, while for group 2 (∼35% of sample) were driven almost entirely by THC potency. Conclusion This study found that consumer preferences for ∼65% of consumers of cannabis edibles are being met through regulated channels. The remaining ∼35% are driven by THC potency at levels that are not currently available on the licensed market. Attracting this market segment will require reviewing the risks and benefits of restricting THC package content.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".