Attitudes and practice patterns of Canadian physiatrists regarding medical cannabis
Bibliographic record
Abstract
Objective: To assess practice patterns and attitudes of Canadian physiatrists, given their expertise in pain management and spasticity, conditions in which medical cannabis (MC) should be considered. Design: A 24-item, survey questionnaire was sent to physiatrists across Canada. Subjects: One hundred and nine physiatrists responded. Methods: A structured web-based survey distributed to members of Canadian Association of Physical Medicine and Rehabilitation. Inferential statistical analysis was conducted. Results: A majority of respondents acknowledged the medicinal value of MC, with 61% of respondents feeling comfortable discussing it, whereas only 31% felt comfortable authorizing MC. Years of work experience did not impact comfort regarding discussions of MC, but those with 21+ years of experience authorized MC more frequently. A significant relationship was observed between subspecialty and MC prescribing; most prescriptions authorized for neuropathic pain, musculoskeletal pain and spasticity. Most respondents agreed that medical school and residency programs provided insufficient education on MC, and that governmental and institutional guidelines remained unclear. Conclusion: Addressing cannabinoids in medical school and residency is important to improve the therapeutic and counselling aspects of patient care in addressing safety and preventing misuse. With clearer guidelines and more research on MC efficacy, physiatrists will be more knowledgeable and better able to improve patient lives.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".