Substance Use Disorder and Harm Reduction Curriculum in United States and Canadian Undergraduate Medical Education: An Online Survey
Bibliographic record
Abstract
BACKGROUND: Substance use disorders (SUD) are a significant public health challenge, necessitating that clinicians are trained in SUD treatment and harm reduction (HR) strategies. Despite this, no studies have assessed the extent of SUD and HR training across all medical schools. This study assesses the current state of SUD and HR curriculum among medical students in the United States and Canada. METHODS: From May to July 2023, we conducted an anonymous online survey via email invitation to student affairs' offices of all 220 accredited US and Canadian medical schools. The survey assessed the curricula students were exposed to related to SUD treatment, HR, and stigmatizing attitudes. RESULTS: A sample of 568 students from 52 medical schools (23.6% of all US and Canadian medical schools) completed the survey. Participants reported that in their medical school they were taught about: recognition of an opioid overdose (80.0%), identifying and treating opioid withdrawal (68.2%), principles and practices of HR (60.6%), administering naloxone (56.6%), the importance of syringe service programs (51.8%), prescribing methadone and/or buprenorphine (29.5%), and counseling patients on safe injection practices (11.4%). In addition, participants reported that they were taught: how to identify drug-seeking behavior (36.4%), that people who use heroin are "drug abusers" (24.4%), to withhold opioid pain medication from patients who are known or suspected to use drugs (15.9%), and that medication for opioid use disorder is another form of addiction (12.6%). DISCUSSION: We found large curricular gaps related to the administration of medications for opioid use disorder and treating opioid overdose and withdrawal, as well as a significant prevalence of stigmatizing attitudes. Renewed efforts are needed to implement comprehensive and destigmatizing SUD curricula. The study is limited by response bias and is expected to overestimate the extent of HR related curriculum, indicating the true gap is likely higher than reported.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.003 | 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".