<scp>Students‐As‐Teachers</scp> : Fostering medical educators
Bibliographic record
Abstract
BACKGROUND: While mounting evidence supports various benefits of Students-As-Teachers (SAT) curricula in preparing students to teach, limited SAT electives are offered across Canada. We developed a 4-week SAT selective for fourth-year medical students at the University of Toronto to enhance medical education knowledge and teaching skills. This study aimed to evaluate the SAT programme and its impact on students' development as educators, their experience as learners and educators, and their future plans for involvement with medical education. APPROACH: Students participated in highly interactive small group seminars and teaching opportunities in nonclinical and clinical environments. Course evaluation consisted of pre-selective and post-selective surveys and written reflections on the selective experience and future career aspirations. A theory-based evaluation approach was utilized to compare the SAT programme's theory with course outcomes. EVALUATION: Post-SAT selective, students self-reported greater knowledge and confidence in teaching methods, provision of feedback, medical education scholarship, and interest in further medical education training. Student reflections highlighted three key themes. Identity formation as educators and the importance of mentorship in medical education aligned with our programme theory, while an unexpected outcome included a shifting perception on teaching and feedback from a learner to an educator lens. IMPLICATIONS: This study's findings demonstrate the ability of SAT curricula to build capacity for future medical educators. Positive factors contributing to the programme's outcomes included cohort size, course and seminar structure, and active group participation. Future iterations may explore use of flipped classroom models, additional clinical teaching opportunities, and near-peer teaching.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".