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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".