Levels of support and consumer perceptions of cannabis regulations in Canada
Bibliographic record
Abstract
Background: Canada legalized cannabis for adult (recreational) use in 2018, alongside regulations on the sale, use, and possession of cannabis. To date, there is little evidence on consumer perceptions and support of cannabis regulations.Objectives: This study examined perceptions of nine cannabis regulatory policies, including differences by cannabis consumption and provincial policy.Methods: National survey data were analyzed from Wave 5 of the International Cannabis Policy Study conducted online in 2022 with 16,812 Canadians aged 16+ years, 62% of which were assigned female-at-birth. Weighted logistic regression models examined support for nine policy variables.Results: Support among Canadians was greatest for health warnings on cannabis products (62.6%), legalization for adult use (58.5%), and retail store window-coverings (49.2%), followed by a vaping/extract THC limit (40.1%), retail store density (35.5%), government-only store models (34.6%), the THC limit on edibles (32.3%), and advertising restrictions (31.8%). The 30 g purchasing limit had the least consumer support (10.1%). As consumption increased, opposition generally increased, although support remained high among consumers. Compared to non-consumers, daily consumers were more likely to oppose window-coverings (OR = 1.43, CI95 = 1.16–1.75, p = .001). Where policies differed provincially, few differences in support were observed. No differences in support for THC limits on vaping/extracts were observed between Newfoundland, Nova Scotia, and Quebec versus the rest of Canada, despite stronger vaping/extract regulations (OR = 1.05, CI95 = 0.87–1.28, p = .597).Conclusion: Canadians generally support existing cannabis regulations that were implemented to support public health. The high level of support among consumers suggests that the comprehensive regulations may not undermine transitions to legal retail sources.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".