Changes in Medical Cannabis Use After Recreational Cannabis Legalization in Canada
Bibliographic record
Abstract
Background: As part of its recreational cannabis legalization in October 2018, Canada imposed an excise tax of 10% (or $1 a gram, whichever is higher) on both recreational and medical cannabis. There is little evidence to inform the ongoing debate on whether the legalization had adverse impacts on medical cannabis use. Methods: We used an interrupted time series design and data on medical cannabis shipments (i.e., mail-order deliveries of cannabis from a licensed producer to a patient authorized to obtain medical cannabis) in Canada between quarter 1 of 2014 and quarter 1 of 2020. We examined changes in medical cannabis shipments after Canada's recreational cannabis legalization both across Canada and for each province. As this study used publicly available, province-level aggregate data, ethics approval was not required. Results: Recreational cannabis legalization was associated with significant reductions in medical cannabis use in 7 out of 10 Canadian provinces. Compared with the counterfactual estimated from prelegalization trends, the reduction in quarter 1 of 2020 varied from 500 shipments per 100,000 population (95% CI=312–688 shipments per 100,000 population) or 32% (95% CI=22–43%) in Newfoundland and Labrador to 3,778 shipments per 100,000 population (95% CI=2,972–4,585 shipments per 100,000 population) or 74% (95% CI=68–79%) in Alberta. At the national level, the number of medical cannabis shipments decreased by 823 per 100,000 population (95% CI=725–921 shipments per 100,000 population) or 48% (95% CI=45–52%). Conclusions: Recreational cannabis legalization was associated with reductions in medical cannabis use. Our findings call for policy attention to address possible adverse impacts of recreational cannabis legalization on medical cannabis users.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 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".