Prevalence and self-reported reasons of cannabis use for medical purposes in USA and Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 it