Stigma‐related barriers to medical cannabis as harm reduction for substance use disorder: Obstacles and opportunities for improvement
Bibliographic record
Abstract
Emerging evidence on substituting cannabis for more harmful drugs has led to cannabis becoming a novel harm-reduction strategy for combating the current drug poisoning crisis. However, the authorization of medical cannabis as part of a harm-reduction approach and recovery strategy has significant implementation barriers rooted in longstanding stigma towards cannabis. Through a multi-discipline collaboration of Canadian clinicians and academic researchers, we highlighted stigma barriers and opportunities to address these barriers to elicit improved delivery of medical cannabis as a harm-reduction therapy within existing therapeutic frameworks. Evidence from existing literature and real-world experiences converged on three key themes related to stigma barriers: (1) Lack of medical cannabis education within the healthcare community, (2) lack of consensus and coordination among harm-reduction services and (3) access to medical cannabis. We highlight potential solutions to these issues, including improved healthcare education, better coordination between care teams and suggestions for improving access. Through this discussion, we hope to contribute to reducing the stigma around using medical cannabis as a harm-reduction strategy for individuals with a substance use disorder and consider new perspectives in policy development surrounding recovery services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.044 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.015 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 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".