Why medical students should learn how to teach
Bibliographic record
Abstract
BACKGROUND: We reviewed the medical-education literature in order to explore the significance and importance of teaching medical students about education principles and teaching skills. AIMS: To discuss reasons why formal initiatives aimed at improving teaching skills should be part of the training of all physicians, and how it could begin at the medical-student level. DESCRIPTION: In this article, we propose several reasons that support formal undergraduate medical training in education principles: (1) medical students are future residents and faculty members and will have teaching roles; (2) medical students may become more effective communicators as a result of such training, as teaching is an essential aspect of physician-patient interaction; and (3) medical students with a better understanding of teaching and learning principles may become better learners. We suggest that exposure to teaching principles, skills, and techniques should be done in a sequential manner during the education of a physician, starting in medical school and continuing through postgraduate education and into practice. We outline learning objectives, teaching strategies, and evaluation methods for medical-education components in an undergraduate curriculum. CONCLUSION: Medical students' informal teaching activities accompany, facilitate, and complement many important aspects of their medical education. Formally developing medical students' knowledge, skills, and attitudes in education may further stimulate these aspects.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".