Residents-as-Teachers Programs in Psychiatry: A Systematic Review
Bibliographic record
Abstract
OBJECTIVES: Because psychiatry residents have important roles as teachers and significant opportunities to contribute to medical student education, we set out to: identify all randomized control trials (RCT) for residents' teaching skills programs in psychiatry and to identify the efficacy of those interventions for improving teaching skills; identify the strengths and weaknesses of the available studies across medical disciplines; and identify currently available methods for enhancing residents' teaching skills for residents training in psychiatry. METHODS: The published English-language literature was searched using PubMed, Social Sciences Index, and PsycINFO databases, with key search words including: residents, teaching skills, residents as teachers, psychiatry, and assessments. Both RCT and controlled, nonrandomized trials of residents' teaching programs directed to enhance residents' teaching skills were selected and critically appraised. RESULTS: Of 13 trials identified and reviewed, most included residents in internal medicine. Only one included psychiatry residents and assessed their ability to teach interviewing skills to medical students. Along with other studies, this study demonstrated improvement in residents' teaching skills. Overall, interventions and outcome measures were heterogeneous while the quality of methodologies varied. Five studies were of higher quality, representing examples of quality educational research. Several described group differences, blinding, good follow-up, and use of valid, reliable tools. CONCLUSIONS: Only one trial exists that incorporated psychiatry residents. Significant opportunity to advance educational research in this field exists. Psychiatry residency program directors should incorporate high-quality methodologies and can benefit from the findings of trials in other disciplines.
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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 | 0.014 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".