Teaching Medical Students to Teach: A Narrative Review and Literature-Informed Recommendations for Student-as-Teacher Curricula
Bibliographic record
Abstract
PURPOSE: Medical students play an important role as teachers. This narrative review synthesizes the student-as-teacher (SaT) literature that incorporates formal teaching skills training, with the goal of proposing literature-informed recommendations for SaT curriculum development within the context of competency-based medical education. METHOD: In January and August 2020, the authors searched Medline and Embase and conducted forward citation searches in Scopus to identify articles from 1985 to 2020 related to teaching medical students to teach. Search terms included "student(s) as teacher(s)," "near-peer teaching," and "teaching to teach." The findings from selected articles were summarized and synthesized. RESULTS: Forty-three articles met the inclusion criteria: 33 papers describing specific SaT curricula, 3 literature reviews, 6 stakeholder surveys, and 1 Delphi study. While students often engage in teaching roles, formal teaching skills training is not always provided. Existing curricula are usually optional, delivered as short courses, and offered to senior students. Course content commonly includes topics related to planning and delivering learning activities and providing feedback. Of the numerous teaching modalities employed, students indicate a preference for interactive and experiential learning. Methods used to assess students' teaching skills vary; however, direct observation and feedback are valued assessment methods. Program evaluations have shown positive outcomes, including high student satisfaction and enhanced self-confidence as teachers. CONCLUSIONS: Informed by these findings and outcome-based educational frameworks, the authors suggest 4 guiding principles for SaT curricula: (1) teaching skills training should be formally implemented in undergraduate medical education and should, whenever possible, be compulsory; (2) teaching skills should be taught longitudinally and progressively along the undergraduate to postgraduate continuum; (3) curriculum content should be aligned with a school's preexisting curriculum and local context; and (4) authentic opportunities for students to engage in teaching roles should be included. They also propose recommendations for curriculum content, teaching modalities, and assessment methods.
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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 | 0.030 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".