Lifelong learning in ethical practice: A challenge for continuing medical education
Bibliographic record
Abstract
BACKGROUND: Formal education in the identification, analysis, and resolution of ethical issues in clinical practice is now an essential component of undergraduate and postgraduate medical education. Physicians educated before the 1980s have had little or no formal education in ethics. This article describes a project for assessing the content and format appropriate for the continuing education needs of practicing physicians. METHODS: A questionnaire and follow-up facilitated small-group discussions with a physician ethicist around case-based problems were used to identify the ethical issues in practice where participants felt the need for continuing education. RESULTS: The project confirmed that practitioners had very little formal ethics in medical school and less since starting practice despite encountering ethical issues. The most frequently used method of learning about ethics was informal discussion among those who have the same lack of formal education. Physicians did not feel that they needed a "very high" level of confidence and competence in handling ethical issues, even those commonly encountered. Participants indicated strongly that they lacked a systematic approach to the identification and analysis of ethical issues and suggest incorporation of the ethical component into regular CME. FINDINGS: In spite of the small study population and the volunteer nature of the participants, the project demonstrated the identification of ethics content for CME similar to that used in medical education. Further work is needed to assess objective needs for ethics education in addition to the perceived needs of clinicians.
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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 | 0.114 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.024 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.019 | 0.019 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".