Medicinal Cannabis Use for Rheumatic Conditions in the <scp>US</scp> Versus Canada: Rationale for Use and Patient–Health Care Provider Interactions
Bibliographic record
Abstract
OBJECTIVE: Understanding how medical cannabis (MC) use is integrated into medical practice for rheumatic disease management is essential. We characterized rationale for MC use, patient-physician interactions around MC, and MC use patterns among people with rheumatic conditions in the US and Canada. METHODS: We surveyed 3406 participants with rheumatic conditions in the US and Canada, with 1727 completing the survey (50.7% response rate). We assessed disclosure of MC use to health care providers, MC authorization by health care providers, and MC use patterns and investigated factors associated with MC disclosure to health care providers in the US versus Canada. RESULTS: Overall, 54.9% of US respondents and 78.0% of Canadians reported past or current MC use, typically because of inadequate symptom relief from other medications. Compared to those in Canada, fewer US participants obtained MC licenses, disclosed MC use to their health care providers, or asked advice on how to use MC (all P values <0.001). Overall, 47.4% of Canadian versus 28.2% of US participants rated their medical professionals as their most trusted information source. MC legality in state of residence was associated with 2.49 greater odds of disclosing MC use to health care providers (95% confidence interval: 1.49-4.16, P < 0.001) in the US, whereas there were no factors associated with MC disclosure in Canada. Our study is limited by our convenience sampling strategy and cross-sectional design. CONCLUSION: Despite widespread availability, MC is poorly integrated into rheumatic disease care, with most patients self-directing use with minimal or no clinical oversight. Concerted efforts to integrate MC into education and clinical policy is critical.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".