Addiction Medicine Training Fellowships in North America: A Recent Assessment of Progress and Needs
Bibliographic record
Abstract
OBJECTIVES: Although unhealthy substance use and addiction contribute to 1 in 4 deaths and are estimated to cost the US more than $740 billion annually, fewer than 12 hours of physician education over the 7 years of medical school and primary residency training specifically address alcohol and other drug-related issues. Addiction Medicine was formally recognized as a medical subspecialty in 2016 to address the need for physicians trained in prevention, treatment, and management of substance use. This study examines the characteristics of the Addiction Medicine fellowships in operation during this critical period in the subspecialty's development to identify needs and potential. METHODS: This study is a cross-sectional survey of Addiction Medicine Fellowship Directors from 46 fellowships accredited as of 2017 (43 in the United States and 3 in Canada). The response rate was 100%. RESULTS: Directors estimated significant growth in available fellowship slots between 2016 to 2017 and 2017 to 2018 (F = 49.584, P < .001). The majority of Directors reported that demand for their graduates was high (79.5%). Fellow training in screening, brief intervention, and referral to treatment spanned many substances and age groups, although fewer programs focused on nicotine and on adolescent populations. Notably, most directors reported that graduates completed waiver training to prescribe buprenorphine-naloxone (77.5%) and gained clinical experience in an opioid treatment setting (89.1%). Funding was the #1 need among 56.8% of Directors. CONCLUSIONS: Despite significant growth in Addiction Medicine fellowships over the past 6 years, meeting future workforce demands for Addiction Medicine specialists depends on access to funding to support fellowships.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".