Bibliographic record
Abstract
Medical schools and residency programs have become very adept at teaching medical students and residents an enormous amount of information. However, it is much less clear whether they are effective at fostering virtuous qualities like empathy or professionalism in trainees. This would come as no surprise to Plato, who famously argued in the Meno that virtue cannot be taught. This pedagogical challenge threatens to stymie medical educators, who increasingly recognize the importance of professionalism, compassion, and empathy in the practice of good medicine. As medical educators, we are motivated to demonstrate that virtue is teachable and to find a way to do so, as this is how we will be able to improve the conduct of physicians and the quality of their care of patients. As such, we address the question of the teachability of virtue in the realm of medicine, analysing Plato's contradictory analyses in the Meno and Protagoras, and drawing upon modern neuroscience to turn an empirical lens on the question. We explore the ways in which Noddings' Ethic of Care may offer a way forward for medical educators keen to foster virtue in trainees. We conclude by demonstrating how, by harnessing the power of caring relationships, the principles of Noddings' Ethic of Care have already been applied to medical education at a university in Israel.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".