Assessing ethical problem solving by reasoning rather than decision making
Bibliographic record
Abstract
CONTEXT: The assessment of ethical problem solving in medicine has been controversial and challenging. The purposes of this study were: (i) to create a new instrument to measure doctors' decisions on and reasoning approach towards resolving ethical problems; (ii) to evaluate the scores generated by the new instrument for their reliability and validity, and (iii) to compare doctors' ethical reasoning abilities between countries and among medical students, residents and experts. METHODS: This study used 15 clinical vignettes and the think-aloud method to identify the processes and components involved in ethical problem solving. Subjects included volunteer ethics experts, postgraduate Year 2 residents and pre-clerkship medical students. The interview data were coded using the instruments of the decision score and Ethical Reasoning Inventory (ERI). The ERI assessed the quality of ethical reasoning for a particular case (Part I) and for an individual globally across all the vignettes (Part II). RESULTS: There were 17 Canadian and 32 Taiwanese subjects. Based on the Canadian standard, the decision scores between Taiwanese and Canadian subjects differed significantly, but made no discrimination among the three levels of expertise. Scores on the ERI Parts I and II, which reflect doctors' reasoning quality, differed between countries and among different levels of expertise in Taiwan, providing evidence of construct validity. In addition, experts had a greater organised knowledge structure and considered more relevant variables in the process of arriving at ethical decisions than did residents or students. The reliability of ERI scores was 0.70-0.99 on Part I and 0.75-0.80 on Part II. CONCLUSIONS: Expertise in solving ethical problems could not be differentiated by the decisions made, but could be differentiated according to the reasoning used to make those decisions. The difference between Taiwanese and Canadian experts suggests that cultural considerations come into play in the decisions that are made in the course of providing humane care to patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.137 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 teacher head, 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".