Scholarly Conversations in Medical Education
Bibliographic record
Abstract
This supplement includes the eight research papers accepted by the 2016 Research in Medical Education Program Planning Committee. In this Commentary, the authors use "conversations in medical education" as a guiding metaphor to explore what these papers contribute to the current scholarly discourse in medical education. They organize their discussion around two domains: the topic of study and the methodological approach. The authors map the eight research papers to six "hot topics" in medical education: (1) curriculum reform, (2) duty hours restriction, (3) learner well-being, (4) innovations in teaching and assessment, (5) self-regulated learning, and (6) learning environment, and to three purposes commonly served by medical education research: (1) description, (2) justification, and (3) clarification. They discuss the range of methods employed in the papers. The authors end by encouraging educators to engage in these ongoing scholarly conversations.
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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 | 0.031 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".