A review of teaching skills development programmes for medical students
Bibliographic record
Abstract
CONTEXT: The CanMEDS role of Scholar requires that medical trainees develop their skills as medical educators. The development of teaching skills in undergraduate medical students is therefore desirable, especially in view of the teaching obligations in residency programmes. OBJECTIVES: The goal of this review was to identify the characteristics and outcomes of programmes designed to develop the teaching skills of undergraduate medical students. METHODS: The authors searched medical literature databases using combinations of the search terms 'medical student', 'teacher', 'teaching skills', 'peer teaching', 'near-peer teaching' and 'student as teacher'. Twenty papers fit the predetermined search criteria, which included original characterisations of specific programmes involving undergraduate medical students. RESULTS: Three types of initiative were identified in the reviewed articles: peer teaching programmes; teaching workshops, and community outreach programmes. The majority of study participants were students in Years 3 and 4. Subjective self-evaluation by participants using Likert scale-based surveys was by far the most commonly used method of measuring project outcomes. Objective, quantitative teaching-related outcomes were rarely noted in the reports reviewed. Self-perceived improvements in teaching skills were noted by participants in most of the reports. Other perceived benefits included increases in organisational skills, knowledge and confidence in giving feedback. CONCLUSIONS: Although several types of programmes have been shown to subjectively improve the teaching skills of undergraduate medical students, characterisation of the objective outcomes of these initiatives is lacking and requires further study.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".