Attitudes toward medical cannabis among family physicians practising in Ontario, Canada: a qualitative research study
Bibliographic record
Abstract
BACKGROUND: Medical cannabis has been legally available in Canada since 2001, but its benefits and harms remain uncertain. We explored attitudes toward medical cannabis among family physicians practising in Ontario. METHODS: Between January and October 2019, we conducted a qualitative study of Ontario family physicians using semistructured telephone interviews. We applied thematic analysis to interview transcripts and identified representative quotes. RESULTS: Eleven physicians agreed to be interviewed, and 3 themes regarding medical cannabis emerged: reluctance to authorize use, concern over harms and lack of practical knowledge. Participants raised concerns about the limited evidence for, and their lack of education regarding, the therapeutic use of cannabis, particularly the harms associated with neurocognitive development, exacerbation of mental illness and drug interactions in older adults. Some participants thought medical cannabis was overly accessible and questioned their role following legalization of recreational cannabis. INTERPRETATION: Despite the increasing availability of medical cannabis, family physicians expressed reluctance to authorize its use because of lack of knowledge and concerns regarding harms. Family physicians may benefit from guidance and education that address concerns they have surrounding medical cannabis.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".