Clinician–Scientist Training in Addiction Medicine: A Novel Program in a Canadian Setting
Bibliographic record
Abstract
To the Editor: Medical education has long been the missing element in the response to the global addiction problem. Instead of treating addiction as a disease, governments have focused on drug prohibition and law enforcement. This approach has failed by many measures, and, as a result, millions of people have suffered. Addiction science has increasingly identified a range of evidence-based approaches to treat substance use disorders, particularly through early identification and treatment. However, most interventions are underused. Better physician education can improve accurate use of evidence-based treatments. The demand for training outstretches our capacity by 30% to 40% and poses a serious challenge to our vision of training the next generation of addiction doctors. To address this unmet demand, training in addiction medicine should be standardized and scaled up.1 The key components of such a training program should include clinical experiences in detoxification protocols, inpatient consultation, youth addiction and mental health, residential treatment focused on women, chronic pain management, and longitudinal community-based treatment. Ideally, all health care providers involved in care of people with substance use disorders should take it, including those in primary care or internal medicine, social work, and nursing. To this end, a promising initiative has recently been launched at the University of British Columbia and St. Paul’s Hospital—that is, a large addiction medicine fellowship. The fellowship provides 12 months of specialized training for physicians from general practice, internal medicine, and psychiatry. The Addiction Medicine Foundation (AMF) accredited the fellowship, and alumni are encouraged to write AMF exams. In addition, they obtain research training through intensive mentorship and quality opportunities to develop and publish research manuscripts through academic half-days, journal clubs, and conferences. They receive media training, learn to influence public policy, advocate for patients, and lead academic research projects. Most health systems do not specifically train generalist doctors in addiction medicine; when they do, this is mostly privileged to a handful of psychiatry programs that train a definite number of addiction psychiatrists. Our fellowship trains family physicians, internists, and individuals from other disciplines and thus expands the specialist treatment workforce to professionals frequently seeing people with substance use disorders who are well positioned to bridge the implementation gap.2 Access to effective treatments grows. Jan Klimas, MSc, PhDPostdoctoral fellow, British Columbia Centre on Substance Use, Department of Medicine, University of British Columbia, St. Paul’s Hospital, Vancouver, British Columbia, Canada, and postdoctoral fellow, School of Medicine, University College Dublin, Dublin, Ireland; [email protected]; ORCID: http://orcid.org/0000-0002-5179-0052. Ryan McNeil, PhDResearch scientist, British Columbia Centre on Substance Use, and assistant professor, Department of Medicine, University of British Columbia, St. Paul’s Hospital, Vancouver, British Columbia; ORCID: http://orcid.org/0000-0001-9555-7693. Will Small, PhDResearch scientist, British Columbia Centre on Substance Use, Department of Medicine, University of British Columbia, St. Paul’s Hospital, and interim director, Centre for Applied Research in Mental Health and Addiction (CARMHA), and assistant professor, Faculty of Health Sciences, Simon Fraser University, Vancouver, British Columbia, Canada. Walter Cullen, MDProfessor of urban general practice, School of Medicine, University College Dublin, Health Sciences Centre, Belfield, Dublin, Ireland; ORCID: http://orcid.org/0000-0003-1838-5052.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.028 | 0.008 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.015 | 0.019 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".