Dynamic Tensions Following New Pedagogy in Undergraduate Medical Education
Bibliographic record
Abstract
The authors draw on their many decades of combined experience with medical students, observing their maturation into practice in widely differing contexts, to reaffirm some of the essential goals of medical education. They briefly review curricular changes in medical education over the past 100 years, then focus on the dynamic tension in undergraduate medical education (UME) resulting from new pedagogy. Specifically, these tensions arise from the differing trajectories and directions of the 3 traditional pillars of academic medicine: clinical excellence, state-of-the-art education, and cutting-edge research. The authors highlight the role of generalism as an essential foundation of UME, as well as the dilemma of a shrinking cadre of medical students choosing a generalist career path. To address challenges stemming from pedagogical changes, the authors offer 4 observations. First, a more condensed approach to faculty development may be to ensure that bringing teachers up to speed on the new curriculum is not excessively burdensome. Second would be a more gradual introduction of the proposed changes. Third, some discussion about medical education pedagogy and curricular development ought to have a place in UME to prepare the next generation of physicians for ongoing changes in accreditation and in approaches to education. Finally, more appropriate funding of medical education would alleviate some of the burden and anxiety by acknowledging its nonmaterial value.
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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 | 0.027 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.031 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".