Twelve tips for preparing residents as teachers
Bibliographic record
Abstract
BACKGROUND: Residents are frequently identified by medical students as their most frequent and memorable teachers; residents also teach their peers, junior and senior colleagues, other health professionals, and their patients. Many will teach in their future practice. Developing the skills to become a teacher is an important part of postgraduate education, and warrants a systematic, planned approach that may include many complementary learning opportunities. AIMS: Our purpose is to describe one such approach: a 4-week elective experience in medical education offered to postgraduate learners. METHOD: The paper describes the background and goals for the elective, and the various steps in planning, implementing, and evaluating such a course, drawing on the literature and mining our own experience for examples. Specifically, we address the following: needs assessment; the determination and selection of content, sequence, and teaching and learning methods; the experiential learning opportunities offered; and the emphasis on the participants' developing self-awareness of themselves as teachers, and as part of a community of teachers. RESULTS: The program implementation, program evaluation, and response to feedback received are described. CONCLUSION: A 4-week elective experience in medical education was positively received by participants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".