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Record W3019025469 · doi:10.1111/jep.13398

Can doctors be taught virtue?

2020· article· en· W3019025469 on OpenAlexaff
Ariel Lefkowitz, Dafna Meitar, Ayelet Kuper

Bibliographic record

VenueJournal of Evaluation in Clinical Practice · 2020
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVirtueCompassionEmpathySurpriseVirtue ethicsPsychologyMedical educationRealmEpistemic virtueQuality (philosophy)EpistemologyEngineering ethicsPedagogySociologyMedicineSocial psychologyPhilosophyLawPolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.215
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.493
Threshold uncertainty score0.792

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.215
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.205
GPT teacher head0.545
Teacher spread0.340 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2020
Admission routes1
Has abstractyes

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