Access to cannabis retail stores across Canada 6 months following legalization: a descriptive study
Bibliographic record
Abstract
BACKGROUND: On Oct. 17, 2018, Canada legalized recreational cannabis with the dual goals of reducing youth use and eliminating the illicit cannabis market. We examined factors associated with access to physical cannabis stores across Canada 6 months following legalization. METHODS: We extracted the address and operating hours of all legal cannabis stores in Canada from online government and private listings. We conducted a descriptive study examining the association between private/hybrid (mixture of government and private stores) and government-only retail models with 4 measures of physical access to cannabis: store density, weekly hours of operation, median distance to the nearest school and relative availability of cannabis stores between low- and high-income neighbourhoods. RESULTS: Six months after legalization, there were 260 cannabis retail stores across Canada: 181 privately run stores, 55 government-run stores and 24 stores in the hybrid retail system. Compared to jurisdictions with a government-run model, jurisdictions with a private/hybrid retail model had 49% (95% confidence interval 10%-200%) more stores per capita, retailers were open on average 9.2 more hours per week, and stores were located closer to schools (median 166.7 m). In both retail models, there was over twice the concentration of cannabis stores in neighbourhoods in the lowest income quintile compared to the highest income quintile. INTERPRETATION: Marked differences in physical access to cannabis retail are emerging between jurisdictions with private/hybrid retail models and those with government-only retail models. Ongoing surveillance including monitoring differences in cannabis use and harms across jurisdictions is needed.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".