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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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, 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".