In-Hospital Training in Addiction Medicine: A Mixed-Methods Study of Health Care Provider Benefits and Differences
Bibliographic record
Abstract
Background : Hospital-based clinical addiction medicine training can improve knowledge of clinical care for substance-using populations. However, application of structured, self-assessment tools to evaluate differences in knowledge gained by learners who participate in such training has not yet been addressed. Methods : Participants ( n = 142) of an elective with the hospital-based Addiction Medicine Consult Team (AMCT) in Vancouver, Canada, responded to an online self-evaluation survey before and immediately after the structured elective. Areas covered included substance use screening, history taking, signs and symptoms examination, withdrawal treatment, relapse prevention, nicotine use disorders, opioid use disorders, safe prescribing, and the biology of substance use disorders. A purposefully selected sample of 18 trainees were invited to participate in qualitative interviews that elicited feedback on the rotation. Results : Of 168 invited trainees, 142 (84.5%) completed both pre- and post-rotation self-assessments between May 2015 and May 2017. Follow-up participants included medical students, residents, addiction medicine fellows, and family physicians in practice. Self-assessed knowledge of addiction medicine increased significantly post-rotation (mean difference in scores = 11.87 out of the maximum possible 63 points, standard deviation = 17.00; P < .0001). Medical students were found to have the most significant improvement in addiction knowledge (estimated mean difference = 4.43, 95% confidence interval = 0.76, 8.09; P = .018). Illustrative quotes describe the dynamics involved in the learning process among trainees. Conclusions : Completion of a hospital-based clinical elective was associated with improved knowledge of addiction medicine. Medical students appear to benefit more from the addiction elective with a hospital-based AMCT than other types of learners.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".