Lessons from 20 years of medical cannabis use in Canada
Bibliographic record
Abstract
BACKGROUND: Canada was one of the first countries to regulate the medical use of cannabis. However, literature on Canada's medical cannabis program is limited. METHODS: We use administrative data from the medical cannabis program, and licensed cannabis vendor catalog data to describe a) the participation of patients, physicians, and cannabis vendors in the program from its inception in 1999 to 2021, and b) trends in medical cannabis consumption, prices and potency. We also use national surveys conducted over the last several decades to estimate trends in regular cannabis use (medical or otherwise) and how it changed during the medical cannabis access regimes. RESULTS: In 2001, the Canadian government granted access to those with physician-documented evidence of a severe health problem that could not be managed using conventional therapies. Most patients accessed cannabis grown under a personal production license. By 2013, authorized daily cannabis dosages were very high. In 2014, the government, concerned over illegal diversion, required that cannabis be purchased from a licensed commercial grower; personal production was banned. Physicians were given responsibility for authorizing patient access. To fill the regulatory void, the physician regulatory bodies in Canada imposed their own prescribing restrictions. After these changes, the number of physicians who were willing to support patient cannabis use markedly decline but the number of patients participating in the program sharply increased. Medical cannabis use varied by province-rates were generally lower in provinces with stricter regulations on physician cannabis prescribing. Most varieties of cannabis oil available for sale are now high in CBD and low in THC. Dry cannabis varieties, conversely, tend to be high in THC and low in CBD. Inflation adjusted prices of most varieties of medical cannabis have declined over time. We find that rates of daily cannabis use (medical or otherwise) increased markedly after the 2014 policy regime. The fraction of Canadians using cannabis daily increased again after the 2018 legalization of recreational cannabis; at the same time, participation in the medical access program declined. CONCLUSION: The implications for patient health outcomes of changes in the medical cannabis program and legalization of recreational use remains an important area for future research.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".