Increasing Bioethics Education in Preclinical Medical Curricula: What Ethical Dilemmas Do Clinical Clerks Experience?
Bibliographic record
Abstract
PURPOSE: The increase in bioethics education in preclinical curricula enables medical students to recognize ethical issues and determine right action. The authors sought to explore the ethical dilemmas medical students experience during clinical clerkships. METHOD: Following an e-mail invitation, 100 of 104 graduating medical students allowed their final ethics assignment, a written description of an ethical dilemma experienced during clinical clerkship, to be analyzed. After all identifiers were removed, the narratives underwent qualitative analysis and were then reanalyzed using Jameton's determinants of moral action. RESULTS: Four themes emerged: the clinical service rotation, target, source, and nature of the ethical dilemma. For many clinical clerks, the ethical dilemma arose because they recognized an ethical issue but neither brought it to their supervisors nor resolved it themselves for fear of incurring disfavor. The source of the ethical dilemma was most frequently the student's supervisor (46%), which may explain why, although all narratives demonstrated the Jameton criteria of "moral sensitivity" and 76% demonstrated "moral judgment," only 24% indicated "moral motivation" and only 4% suggested "moral courage." Patients were the most frequent target (76%), followed by students (14%). Students reported informed consent (18%) and inadequate care (17%) as the most common types of dilemmas under the nature theme. CONCLUSIONS: Clinical clerks' experiences of ethical dilemmas might be mitigated if residency education and professional development mirrored the increase in preclinical ethics education, if ethics training included encouraging students to discuss ethical issues as they arise, and if educators developed innovative models of student evaluation.
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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 | 0.013 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".