Perceived access to cannabis and ease of purchasing cannabis in retail stores in Canada immediately before and one year after legalization
Bibliographic record
Abstract
Background: Canada legalized non-medical cannabis in October 2018. Little research has examined the change in perceived access to cannabis after legalization in Canada, including the perceived ease of purchasing cannabis in a legal market. Objectives: To: 1) describe changes in perceived ease of access to cannabis before and one year after legalization; 2) examine associations between perceived ease of cannabis access and cannabis use; and 3) examine associations between perceived ease of purchasing from cannabis stores and cannabis use. Methods: Repeat cross-sectional data come from Canadian respondents aged 16–65 (50% male) in August-October 2018 (n = 10,057) and September-October 2019 (n = 15,256). Respondents were recruited through commercial online panels. Multivariable logistic regression models examined correlates of perceived proximity to retail stores, ease of access, and ease of purchasing from retail stores. Results: Canadians who do not consume cannabis were more likely to report “easy” access to cannabis in 2019 than in 2018 (55% vs. 42%; AOR = 1.80:1.66,1.96). All cannabis consumer groups were more likely to report living 15 minutes or less from a retail store in 2019 than 2018, but the association was strongest among non-consumers in 2019 vs 2018 (AOR = 2.01:183,2.21 vs. AOR = 1.33:1.03,1.73 for daily consumers). Non-daily and daily cannabis consumers were more likely to report it was easy to purchase from an illegal (AOR ranged 1.58–2.22) or legal (AOR ranged 1.31–1.39) store than non-consumers in 2019. Conclusion: Most cannabis consumers and non-consumers perceived access to cannabis as ‘easy’ before legalization and the percentage increased one year after legalization.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".