Changes in medical cannabis use in Ontario, Canada (2014 to 2019): Trajectory of trends, medical authorization patterns and association with recreational cannabis legalization
Bibliographic record
Abstract
Objectives: We characterized trends in medical cannabis use; examined characteristics associated with medical cannabis use without medical authorization; and examined the association between recreational cannabis legalization and medical cannabis use in Ontario, Canada. Methods: Data were from a repeated, population-based, cross-sectional survey of adults (N = 19,543; 2014-2019). Cannabis use was categorized as either medical cannabis use, recreational cannabis use or no cannabis use. The analytical strategy included jointpoint regression, logistic regression and multinomial logistic regression. Results: Medical cannabis use increased from 4 % to 11 % (Annual Percentage Change [APC]: 25 %, 95 % Confidence Interval [CI]: 17 %-33 %) and recreational cannabis use increased from 9 % to 15 % (APC: 9 %, 95 % CI: 3 %-15 %) between 2014 and 2019. Being 18 to 29 years old compared with being 65+ years old was associated with an increased likelihood of medical cannabis use without medical authorization (Odds Ratio [OR]: 4.05, 95 % CI: 2.12-7.72), while being of fair or poor self-perceived health compared with excellent, very good or good self-perceived health (OR: 0.61, 95 % CI: 0.40-0.95) was associated with a decreased likelihood of medical cannabis use without medical authorization. Recreational cannabis legalization was associated with an increased likelihood of medical cannabis use compared with no cannabis use (OR: 1.48, 95 % CI: 1.19-1.85) and of recreational cannabis use compared with no cannabis use (OR: 1.35, 95 % CI: 1.11-1.65). Conclusions: Although medical cannabis use increased, it was largely used without medical authorization. Guidance and education that encourages medical usage under clinical supervision is recommended, and mitigation of known barriers to medical cannabis authorization.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".