A medical ethical reasoning model and its contributions to medical education
Bibliographic record
Abstract
OBJECTIVES: Ethical reasoning in medicine is not well understood and medical educators often find it difficult to justify what and how they teach and assess in medical ethics. To facilitate the development of moral values and professional conduct, a model of ethical reasoning was created. The purposes of this paper are to describe the ethical reasoning model and to indicate how it can be used to foster moral and ethical behaviours. METHODS: The ethical reasoning model was created from information derived from two sources: (i) an examination of different ethical models described in the literature, and (ii) think-aloud interviews with ethical experts in Taiwan and Canada. All the components and cognitive steps used by experts in ethical decision making were extracted and categorised. Interview subjects consisted of 16 voluntary ethics experts. The ethical reasoning models reported in the literature were divided into two groups according to whether they were justification-based or task-based models. Neither of the two types represented the 'whole picture' of ethical reasoning in medicine. This analysis enabled us to identify five universal cognitive steps and the gaps between 'logical decision' and 'action'. RESULTS: The think-aloud interviews verified the multi-dimensional components or steps used by experts when resolving ethical problems. The resulting model, designated the Medical Ethical Reasoning (MER) Model, reflects interactions within three domains: medical and ethical knowledge; cognitive reasoning processes, and attitude. CONCLUSIONS: The MER Model accurately reflects how doctors resolve ethical dilemmas and is seen to be helpful in identifying what and how educators should teach and assess in ethical reasoning. The model can also serve as a communication framework for curricular design. A 'humane' doctor is competent in providing quality, ethical patient care. Making an appropriate ethical decision is the foundation for subsequent ethical behaviours. By contrast with the abundant evidence cited in previous research describing how doctors solve medical problems, there is little empirical evidence indicating how doctors make appropriate ethical decisions. Thus, the cognition of ethical reasoning in medicine is not well understood. This paper represents a step towards overcoming this problem.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".