The Addiction Recovery Clinic: A Novel, Primary-Care-Based Approach to Teaching Addiction Medicine
Bibliographic record
Abstract
PROBLEM: Substance use is highly prevalent in the United States, but little time in the curriculum is devoted to training internal medicine residents in addiction medicine. APPROACH: In 2014, the authors developed and launched the Addiction Recovery Clinic (ARC) to address this educational gap while also providing outpatient clinical services to patients with substance use disorders. The ARC is embedded within the residency primary care practice and is staffed by three to four internal medicine residents, two board-certified addiction medicine specialists, one chief resident, and one psychologist. Residents spend one half-day per week for four consecutive weeks at the ARC seeing new and returning patients. Services provided include pharmacological and behavioral treatments for opioid, alcohol, and other substance use disorders, with direct referral to local addiction treatment facilities as needed. Visit numbers, a patient satisfaction survey, and an end-of-rotation resident evaluation were used to assess the ARC. OUTCOMES: From 2014 to 2015, 611 patient encounters occurred, representing 97 new patients. Sixty-one (63%) patients were seen for opioid use disorder. According to patient satisfaction surveys, 29 (of 31; 94%) patients reported that the ARC probably or definitely helped them to cope with their substance use. Twenty-eight residents completed the end-of-rotation evaluation; all rated the rotation highly. NEXT STEPS: The ARC offers a unique primary-care-based approach to exposing internal medicine residents to the knowledge and skills necessary to diagnose, treat, and prevent unhealthy substance use. Future research will examine other clinical and educational outcomes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".