Teaching skills for medical residents: are these important? A narrative review of the literature
Bibliographic record
Abstract
ABSTRACT BACKGROUND: There is extensive evidence, mainly from the United States and Canada, that points towards the need to train medical residents in teaching skills. Much of the “informal curriculum”, including professional values, is taught by residents when consultants are not around. Furthermore, data from the 1960s show the importance of acquiring these skills, not only for residents but also for all doctors. Teaching moments can be identified in simple daily situations, like discussing a clinical situation with patients and their families, planning patients’ care with the healthcare team or teaching peers and medical students. The aim here was to examine the significance of resident teaching courses and estimate the effectiveness of these courses and the state of the art in Brazil. METHODS: We conducted a review of the literature, using the MEDLINE, PubMed, SciELO and LILACS databases to extract relevant articles describing residents-as-teachers (RaT) programs and the importance of teaching skills for medical residents. This review formed part of the development of a doctoral project on medical education. RESULTS: Original articles, reviews and systematic reviews were used to produce this paper as part of a doctoral project. CONCLUSIONS: RaT programs are important in clinical practice and as role models for junior learners. Moreover, these educational programs improve residents’ self-assessed teaching behaviors and teaching confidence. On the other hand, RaT program curricula are limited by both the number of studies and their methodologies. In Brazil, there is no such experience, according to the data gathered here, except for one master’s thesis.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".