How has access to legal cannabis changed over time? An analysis of the cannabis retail market in Canada 2 years following the legalisation of recreational cannabis
Bibliographic record
Abstract
INTRODUCTION: This study describes the legal recreational cannabis market across Canada over the 2 years following legalisation. We compared changes in access to the legal cannabis retail market for all provinces and territories (jurisdictions) in Canada and explored differences between jurisdictions. METHODS: We collected data for all legal cannabis stores in Canada over five time periods following legalisation in October 2018. We examined the following measures by jurisdiction and retail model (public vs. private operation): absolute and per capita store numbers, hours of operation and store access across neighbourhoods. RESULTS: Two years following legalisation, there were a total of 1183 legal cannabis stores open across Canada (3.7 stores per 100 000 individuals aged 15+). There was wide variation between jurisdictions in access to retail stores, with the lowest stores per capita in Quebec and Ontario (0.6 and 1.6 per 100 000), and the highest in Alberta and Yukon (14.3 per 100 000 in both). Jurisdictions with private retail models had more stores (4.8 vs. 1.0 per 100 000), held greater median weekly hours (80 vs. 69) and experienced greater store growth over time compared to public models. After adjusting for confounders, there were 1.96 times (95% confidence intervals: 1.84, 2.09) more cannabis stores within 1000 m of the lowest- compared to the highest-income quintile neighbourhoods. DISCUSSION AND CONCLUSIONS: While access to the recreational cannabis retail market has increased following legalisation, there is substantial variation in access between jurisdictions and evidence of concentration in lower-income neighbourhoods. These differences may contribute to disparities in cannabis use and harms.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".