Association of recreational cannabis legalization with changes in medical, illegal, and total cannabis expenditures in Canada
Bibliographic record
Abstract
BACKGROUND: Recreational cannabis legalization (RCL) is being adopted by a growing number of jurisdictions internationally. RCL aims to displace the illegal cannabis market and has the potential to disrupt the medical market, yet few studies have examined these dynamics empirically. METHODS: We used interrupted time series analysis to evaluate whether RCL (legislative passage in October 2017/implementation in October 2018) was associated with changes in quarterly national household expenditures on medical cannabis, illegal cannabis, and all cannabis types combined (licensed, illegal, and medical) in Canada from 2001 to 2023, adjusting for price fluctuations. RESULTS: When RCL was passed, medical cannabis represented 11.8 % of the market and illegal cannabis 88.2 %. At five years post-RCL implementation, medical cannabis decreased to 3.7 %, illegal cannabis decreased to 24.3 %, and licensed cannabis took over 72.0 % of the market. The overall cannabis market increased in size by 75 % over these 5 years. Illegal cannabis expenditures increased between RCL passage and implementation but decreased immediately post-implementation and had a significant decreasing trend. Medical cannabis had a significant decreasing trend following RCL passage, and to a lesser extent following RCL implementation. Total cannabis expenditures increased immediately following RCL implementation and showed a significant increasing trend over time. Some caution should be used in interpreting these findings given uncertainty in data quality, particularly for illegal cannabis expenditures (and overall expenditures by extension). CONCLUSIONS: Recreational cannabis legalization in Canada appears to be achieving one of its primary goals by displacing the illegal cannabis market, and medical users also appear to be transitioning to the recreational market. However, overall cannabis expenditures have also grown substantially since legalization, which could have adverse implications for public health.
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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 | 0.004 |
| 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".