Doctors’ and nurses’ perceptions of ethical problems in end‐of‐life decisions
Why this work is in the frame
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Bibliographic record
Abstract
AIMS: To identify and compare doctors' and nurses' perceptions of ethical problems. Rationale. Ethical problems are a source of tension for health professionals. Misunderstandings or conflicts may result from differing perceptions of ethical problems. If true collaboration is to be achieved, it is important to understand the perspectives of others, particularly when difficult end-of-life decisions must be made. METHODS: In this qualitative study a total of seven doctors and 14 nurses working in acute care adult medical-surgical areas, including intensive care, were asked to describe ethical problems that they frequently encounter in practice. Interviews were taped and transcribed. Thematic analysis followed. RESULTS: All participants experienced ethical problems around decision making at the end of life. The core problem for both doctors and nurses was witnessing suffering, which engendered a moral obligation to reduce that suffering. Uncertainty about the best course of action for the patient and family was a source of moral distress. Competing values, hierarchical processes, scarce resources, and communication emerged as common themes. The key difference between the groups was that doctors are responsible for making decisions and nurses must live with these decisions. Each group, therefore, asked different questions when encountering and interpreting sources of moral distress. CONCLUSIONS: It was concluded that observed differences between doctors and nurses were a function of the professional role played by each rather than differences in ethical reasoning or moral motivation. Although this was a small qualitative study on one institution, and may not be generalizable, results suggest that doctors and nurses need to engage in moral discourse to understand and support the ethical burden carried by the other. Administrators should provide opportunities for discourse to help staff reduce moral distress and generate creative strategies for dealing with this.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.007 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it