Marijuana medicine and Canadian physicians: Challenges to meaningful drug policy reform
Bibliographic record
Abstract
There are modest indications of movement in the glacial reform of Canada’s marijuana prohibition. A sign of formal progress is the legal exemption for seriously ill medical users under Health Canada’s evolving Marihuana Medical Access Regulations (MMAR). Several hundred patients have now been approved through an application process that requires support from a medical practitioner or specialist. Physicians are constrained from complying with the MMAR by the highly conservative stance of the Canadian Medical Association (CMA), while the Canadian Medical Protective Association – the body that acts as legal advocates for doctors – maintains a view toward marijuana that, applied to any other drug, would make prescribing even the most routine therapies difficult. On the other hand, it is not clear that physicians really take much interest in their patients’ marijuana use. This article examines the conflict between patients who choose to self‐medicate with marijuana and the community that governs physicians in Canada. It draws on findings from two studies that, respectively, explore doctors’ views on marijuana and the experiences of patients who self‐medicate with cannabis. The inherent conservatism of the medical community – reinforced by lack of interest in how such use might benefit some patients – militates against more learning or widespread acceptance of the use of cannabis as medicine. Nonetheless, the authors argue, doctors ought not to perpetuate the ignoble tradition of ‘Don’t ask – Don’t tell’.
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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 | 0.022 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.014 | 0.021 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".