Evaluation of an Experiential Curriculum for Addiction Education Among Medical Students
Bibliographic record
Abstract
OBJECTIVES: Undergraduate medical education about addictive disease can take many forms, but it is unclear which educational methods are most effective at shaping medical students into physicians who are interested in and competent at addressing addiction. The purpose of this study was to evaluate the efficacy of the Betty Ford Institute's Summer Institute for Medical Students (SIMS), a week-long program aimed at educating medical students about addiction through a combination of traditional didactic and novel experiential sessions. METHODS: A written survey assessing beliefs, attitudes, and practices related to addictive disease was administered to physicians who previously participated in SIMS (n = 140) and to physicians matched for year of graduation from medical school who did not participate in SIMS (n = 105). RESULTS: Compared with their peers, and controlling for sex, age, year of graduation from medical school, specialty, personal experience with addiction, and training in talking to patients about substance use, physicians who participated in SIMS were more likely to believe that they could help addicted patients, find working with addicted patients satisfying, be confident in knowing available resources for addicted patients, believe that addiction is a disease, and be confident in speaking to patients about substance use. Physicians who participated in SIMS were not more likely to practice addiction medicine or to view talking to patients about substance use as clinically relevant. CONCLUSIONS: Undergraduate medical educational interventions combining traditional and experiential programming may render participants better equipped than peers receiving only traditional education to address addiction as physicians.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".