Educational Studies Examining Knowledge of Substance Use Disorders and Career Aspirations Among Medical Trainees in an Inner-City Hospital
Bibliographic record
Abstract
OBJECTIVES: Gaps in addiction medicine training are a reason for poor substance use care in North America. Hospital addiction medicine consult services (AMCS) provide critical medical services, including screening and treatment of substance use disorders. Although these programs often feature an educational component for medical learners, the impact of AMCS teaching on objective knowledge and career aspirations in addiction medicine has not been well described. METHODS: The authors report findings from two sequential studies conducted at a large academic hospital in Vancouver, Canada. The first study assessed the impact of an AMCS clinical rotation on medical trainee addiction medicine objective knowledge using an online survey of 6 true/false questions before and after the rotation. The second study examined the impact of an AMCS rotation on career aspirations using 4 seven-point Likert-type questions. One-sample t tests on mean differences (MD) with Benjamini-Hochberg adjustment for multiple comparisons were employed for statistical analyses. RESULTS: Between May 2017 and June 2018, knowledge scores were significantly higher postrotation (MD = 4.78, standard deviation [SD] = 19.5, P = 0.034) among 115 medical trainees. Between July 2018 and July 2019, aspirations to practice addiction medicine were significantly more favorable postrotation (MD = 3.48, SD = 3.15, P < 0.001) among 101 medical trainees. CONCLUSIONS: AMCS rotations appear to improve addiction medicine knowledge and aspirations to practice addiction medicine among medical trainees. Larger-scale evaluations and outcomes research on integrating substance use disorders teaching in these settings will help move the discipline forward.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".