Understanding medical cannabis use internationally: Why definitions and context matter
Bibliographic record
Abstract
AIMS: To identify variation in identification of medical consumers using alternative self-reported measures and assess whether differences in these rates exist across jurisdictions with different medical policy approaches using evidence from an international study on cannabis use. DESIGN: Secondary analysis of wave 4 (2021) of the International Cannabis Policy Study (ICPS) cross-sectional survey. SETTING: United States, Canada and Australia. PARTICIPANTS: 16 951 (USA 10 472; CAN 5935; AUS 544) respondents who completed the survey and reported past year cannabis use across the three jurisdictions. MEASUREMENTS: Four different medical cannabis use measures were available, and rates of each were estimated using logistic regression methods that adjusted for age, gender, education and ethnicity. Medical cannabis use measures included potentially authorized use (i.e. involving a licensed health professional recommendation, authorization or prescription), pharmaceutical use (i.e. involving a pharmaceutical-grade product), therapeutic use (i.e. to manage physical or mental health conditions) and self-identified medical cannabis use. Country-specific differences were compared and discussed in light of measure and differing cannabis policies. FINDINGS: In wave 4 of the ICPS, 34.0% reported any past year cannabis use, but rates of medical use differed significantly according to the specific question. Far more individuals reported therapeutic use in the past year across all countries [77.3%; 95% confidence interval (CI) = 76.4%-78.2%] than any other measure of medical use. While just over one quarter (28.2%; 95% CI = 27.3%-29.2%) self-identified as a medical user, fewer reported being potentially authorized (22.8%; 95% CI = 22.0%-23.7%) or having a pharmaceutical prescription from a medical professional (12.3%; 95% CI = 11.6%-13.0%). Australians (27.2%; 95% CI = 23.0%-31.4%) and Americans (25.9%; 95% CI = 24.6%-27.2%) were more likely to report potentially authorized use than Canadians (17.3%; 95% CI = 16.1%-18.4%), but only Australians (27.4%; 95% CI = 23.6%-31.2%) reported high levels of prior use of a pharmaceutical-grade cannabinoid. CONCLUSIONS: In the International Cannabis Policy Study, the proportion of respondents (adjusted for demographic factors) who reported medical use varied depending on the measures used within and between countries.
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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 | 0.027 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.007 | 0.018 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".