Impact of a Brief Addiction Medicine Training Experience on Knowledge Self-Assessment among Medical Learners
Bibliographic record
Abstract
BACKGROUND: Implementation of evidence-based approaches to the treatment of various substance use disorders is needed to tackle the existing epidemic of substance use and related harms. Most clinicians, however, lack knowledge and practical experience with these approaches. Given this deficit, the authors examined the impact of an inpatient elective in addiction medicine amongst medical trainees on addiction-related knowledge and medical management. METHODS: Trainees who completed an elective with a hospital-based Addiction Medicine Consult Team (AMCT) in Vancouver, Canada, from May 2015 to May 2016, completed a 9-item self-evaluation scale before and immediately after the elective. RESULTS: A total of 48 participants completed both pre and post AMCT elective surveys. On average, participants were 28 years old (interquartile range [IQR] = 27-29) and contributed 20 days (IQR = 13-27) of clinical service. Knowledge of addiction medicine increased significantly post elective (mean difference [MD] = 8.63, standard deviation [SD] = 18.44; P = .002). The most and the least improved areas of knowledge were relapse prevention and substance use screening, respectively. CONCLUSIONS: Completion of a clinical elective with a hospital-based AMCT appears to improve medical trainees' addiction-related knowledge. Further evaluation and expansion of addiction medicine education is warranted to develop the next generation of skilled addiction care providers.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".