Consumer perceptions of legal cannabis products in Canada, 2019–2021: a repeat cross-sectional study
Bibliographic record
Abstract
Abstract Background Consumer perceptions of legal cannabis products may drive willingness to purchase from the illegal or legal market; however, little is known on this topic. The current study examined perceptions of legal products among Canadian cannabis consumers over a 3-year period following federal legalization of non-medical cannabis in 2018. Methods Data were analyzed from Canadian respondents in the International Cannabis Policy Study, a repeat cross-sectional survey conducted in 2019–2021. Respondents were 15,311 past 12-month cannabis consumers of legal age to purchase cannabis. Weighted logistic regression models examined the association between perceptions of legal cannabis and province of residence, and frequency of cannabis use over time. Results In 2021, cannabis consumers perceived legal cannabis to be safer to buy (54.0%), more convenient to buy (47.8%), more expensive (47.2%), safer to use (46.8%) and higher quality (29.3%) than illegal cannabis. Except for safety of purchasing, consumers had more favourable perceptions of legal cannabis in 2021 than 2019 across all outcomes. For example, consumers had higher odds of perceiving legal cannabis as more convenient to buy in 2021 than 2019 (AOR = 3.09, 95%CI: 2.65,3.60). More frequent consumers had less favourable perceptions of legal cannabis than less frequent consumers. Conclusions Three years since legalization, Canadian cannabis consumers generally had increasingly favourable perceptions of legal vs. illegal products – except for price – with variation across the provinces and frequency of cannabis use. To achieve public health objectives of legalization, federal and provincial governments must ensure that legal cannabis products are preferred to illegal, without appealing to non-consumers.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".