Canadians’ use of cannabis for therapeutic purposes since legalization of recreational cannabis: a cross-sectional analysis by medical authorization status
Bibliographic record
Abstract
BACKGROUND: There has been a precipitous decline in authorizations for medical cannabis since non-medical cannabis was legalized in Canada in 2018. This study examines the demographic and health- and medical cannabis-related factors associated with authorization as well as the differences in medical cannabis use, side effects, and sources of medical cannabis and information by authorization status. METHODS: Individuals who were taking cannabis for therapeutic purposes completed an online survey in early 2022. Multivariable logistic regression was used to determine odds ratios (OR) and 95% confidence intervals (CI) of demographic and health- and medical cannabis-related variables associated with holding medical cannabis authorization. The differences in medical cannabis use, side effects, and sources of information by authorization status were determined via t-tests and chi-squared analysis. RESULTS: A total of 5433 individuals who were currently taking cannabis for therapeutic purposes completed the study, of which 2941 (54.1%) currently held medical authorization. Individuals with authorization were more likely to be older (OR ≥ 70 years vs. < 30 years, 4.85 (95% CI, 3.49-6.76)), identify as a man (OR man vs. woman, 1.53 (1.34-1.74)), have a higher income (OR > $100,000/year vs. < $50,000 year, 1.55 (1.30-1.84)), and less likely to live in a small town (OR small town/rural vs. large city, 0.69 (0.59-0.81)). They were significantly more likely to report not experiencing any side effects (29.9% vs. 23.4%; p < 0.001), knowing the amount of cannabis they were taking (32.1% vs. 17.7%; p < 0.001), obtaining cannabis from regulated sources (74.1% vs. 47.5%; p < 0.001), and seeking information about medical cannabis from healthcare professionals (67.8% vs. 48.2%; p < 0.01) than individuals without authorization. CONCLUSIONS: These findings offer insight into the possible issues regarding equitable access to medical cannabis and how authorization may support and influence individuals in a jurisdiction where recreational cannabis is legalized, highlighting the value of a formal medical cannabis authorization process.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".