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 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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".