Prevalence and self-reported reasons of cannabis use for medical purposes in USA and Canada
Bibliographic record
Abstract
RATIONALE: There has been increasing attention on cannabis use for medical purposes, but there is currently a lack of data on its epidemiology. OBJECTIVES: To examine the epidemiology of self-reported cannabis use for medical purposes by (1) estimating its prevalence, (2) comparing gender and age differences, and (3) investigating what reasons they were used to manage. METHODS: Participants included 27,169 respondents (aged 16-65) who completed Wave 1 of The International Cannabis Policy Study (ICPS) conducted across Canada and the USA in 2018 via online surveys. Cannabis policy conditions were "US legal-recreational" (legal for both recreational and medical uses), "US legal-medical only", "US illegal", and "Canada-medical only". RESULTS: The overall prevalence of self-reported ever cannabis use for medical purposes was 27%, with similar rates by sex and the highest prevalence in young adults. Prevalence was higher in US legal-recreational states (34%) than US illegal states (23%), US legal-medical only states (25%), and Canada (25%). The most common physical health reasons include use to manage pain (53%), sleep (46%), headaches/migraines (35%), appetite (22%), and nausea/vomiting (21%). For mental health reasons, the most common were for anxiety (52%), depression (40%), and PTSD/trauma (17%). There were 11% who reported using cannabis for managing other drug or alcohol use and 4% for psychosis. CONCLUSIONS: A substantial proportion of the North American population self-reported cannabis use for medical purposes for a variety of medical reasons, including those living in jurisdictions without legal markets. Further research is needed to understand the safety and efficacy of these forms of medical cannabis use.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".