Canada’s Recreational Cannabis Legalization and Medical Cannabis Patient Activity, 2017–2022
Bibliographic record
Abstract
Objectives. To estimate changes in medical cannabis patient activity after Canada’s recreational cannabis legalization. Methods. I used linear regressions of interrupted times series models to analyze medical cannabis patient registrations per 10 000 residents, purchases per 100 registrations, and packages per purchase in Canada’s 10 provinces between April 2017 and December 2022. I tested relationships between the recreational law’s passage in June 2018, recreational sales starting in October 2018, and the arrival of edibles and vapes in December 2019. Results. Medical patient registrations initially increased; they slowed after the law passed and started decreasing after edibles became available. Medical purchasing frequencies initially decreased; they decreased further in proportion to recreational sales but stabilized after edibles became available. Medical purchase sizes were initially stable; they began increasing after edibles became available. Conclusions. Canada saw substantial decreases in medical cannabis patient registrations, but the remaining patients stabilized their purchasing frequencies and increased their purchase sizes. Public Health Implications. Other countries might see significant changes in patient usage of their medical cannabis systems after nationwide recreational cannabis legalization. ( Am J Public Health. 2024;114(S8):S673–S680. https://doi.org/10.2105/AJPH.2024.307721 )
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 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".