Virtue and care ethics & humanism in medical education: a scoping review
Bibliographic record
Abstract
PURPOSE: This scoping review explores how virtue and care ethics are incorporated into health professions education and how these factors may relate to the development of humanistic patient care. METHOD: Our team identified citations in the literature emphasizing virtue ethics and care ethics (in PubMed, NLM Catalog, WorldCat, EthicsShare, EthxWeb, Globethics.net , Philosopher's Index, and ProQuest Central) lending themselves to constructs of humanism curricula. Our exclusion criteria consisted of non-English articles, those not addressing virtue and care ethics and humanism in medical pedagogy, and those not addressing aspects of character in health ethics. We examined in a stepwise fashion whether citations: 1) Contained definitions of virtue and care ethics; 2) Implemented virtue and care ethics in health care curricula; and 3) Evidenced patient-directed caregiver humanism. RESULTS: Eight hundred eleven citations were identified, 88 intensively reviewed, and the final 25 analyzed in-depth. We identified multiple key themes with relevant metaphors associated with virtue/care ethics, curricula, and humanism education. CONCLUSIONS: This research sought to better understand how virtue and care ethics can potentially promote humanism and identified themes that facilitate and impede this mission.
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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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.021 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".