Making the Case for History in Medical Education: Fig. 1.
Bibliographic record
Abstract
Historians of medicine have struggled for centuries to make the case for history in medical education. They have developed many arguments about the value of historical perspective, but their efforts have faced persistent obstacles, from limited resources to curricular time constraints and skepticism about whether history actually is essential for physicians. Recent proposals have suggested that history should ally itself with the other medical humanities and make the case that together they can foster medical professionalism. We articulate a different approach and make the case for history as an essential component of medical knowledge, reasoning, and practice. History offers essential insights about the causes of disease (e.g., the non-reductionistic mechanisms needed to account for changes in the burden of disease over time), the nature of efficacy (e.g., why doctors think that their treatments work, and how have their assessments changed over time), and the contingency of medical knowledge and practice amid the social, economic, and political contexts of medicine. These are all things that physicians must know in order to be effective diagnosticians and caregivers, just as they must learn anatomy or pathophysiology. The specific arguments we make can be fit, as needed, into the prevailing language of competencies in medical education.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.035 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".