Cannabis in medicine: a national educational needs assessment among Canadian physicians
Bibliographic record
Abstract
BACKGROUND: There is increasing global awareness and interest in the use of cannabis for therapeutic purposes (CTP). It is clear that health care professionals need to be involved in these decisions, but often lack the education needed to engage in informed discussions with patients. This study was conducted to determine the educational needs of Canadian physicians regarding CTP. METHODS: A national needs assessment survey was developed based on previous survey tools. The survey was approved by the Research Ethics Board of the McGill University Health Centre Research Institute and was provided online using LimeSurvey®. Several national physician organizations and medical education organizations informed their members of the survey. The target audience was Canadian physicians. We sought to identify and rank using 5-point Likert scales the most common factors involved in decision making about using CTP in the following categories: knowledge, experience, attitudes, and barriers. Preferred educational approaches and physician demographics were collected. Gap analysis was conducted to determine the magnitude and importance of differences between perceived and desired knowledge on all decision factors. RESULTS: Four hundred and twenty six responses were received, and physician responses were distributed across Canada consistent with national physician distribution. The most desired knowledge concerned "potential risks of using CTP" and "safety, warning signs and precautions for patients using CTP". The largest gap between perceived current and desired knowledge levels was "dosing" and "the development of treatment plans". CONCLUSIONS: We have identified several key educational needs among Canadian physicians regarding CTP. These data can be used to develop resources and educational programs to support clinicians in this area, as well as to guide further research to inform these gaps.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".