Explorations of a trust approach for nursing ethics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Trust has long been acknowledged as central to nurse-patient relationships. It, however, has not been fully explored normatively. That is, trust must be examined from a perspective that encompasses not only reliability and competence, but also good will within nursing relationships. In this paper, we explore how a trust approach, based on Annette Baier's work on trust in feminist ethics, could help inform future developments in nursing ethics. We discuss the limitations of other approaches such as those based on contracts, paternalism, and care. By drawing out central features of Baier's theory, we demonstrate how it can help overcome the problems of these previous models. In doing so, we emphasise the importance of combining the ethics of care and justice, acknowledging vulnerability and the potential for evil in nursing relationships, and politically situating the ethical concerns of nursing.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.004 | 0.017 |
| 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