How the humanities shape medical culture: Knowing Wegener and other Nazi eponyms
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. While the medical humanities have experienced a renaissance, they are still largely a peripheral component of medical education. This is troublesome because the humanities include a number of disciplines that are foundational in understanding medicine and how it should be practiced. Nonetheless, current medical culture makes it difficult to fully incorporate the humanities into curriculum. We therefore propose an incremental approach to shaping the medical culture that can easily be incorporated into daily teaching as opposed to designing additional classes and resources that must be added to existing educational structures. An example of this approach is reviewed here through teaching historical and ethical lessons surrounding Nazi eponyms. The use of names like Wegener provide brief opportunities for sidebars during clinical lectures to remind learners that empirical data do not provide ethical direction and that our medical history has included atrocities that remind us to practice conscientiously. We provide other examples that can be included in daily learning. This approach eschews the burdens associated with large curricular changes, such as student resistance/apathy and logistical barriers, and can be easily implemented. It also enables change to be gradual and through structures that have already been established, allowing learners to see the benefits of insights from the humanities in small, digestible segments. Through this approach, medical culture can be shaped towards a greater appreciation toward the medical humanities.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".