Ethical practice in nursing: working the in‐betweens
Bibliographic record
Abstract
BACKGROUND: While contemporary ethical theory is of tremendous value to nursing, the extent to which such theory has been informed by the concerns and practices of nurses has been limited. PURPOSE: With a view to complementing extant ethical theory, a study was undertaken to explore, from the perspective of nurses, the meaning of ethics and the enactment of ethical practice in nursing. DESIGN AND METHODS: Located in the interpretive/constructivist paradigm, using an emergent design, this inquiry employed focus groups to collect the data. Eighty-seven nurses from a wide range of practice settings were interviewed in 19 focus groups of three to nine nurses each. FINDINGS: The nurses described ethics in their practice as both a way of being and a process of enactment. They described drawing on a wide range of sources of moral knowledge in a dynamic process of developing awareness of themselves as moral agents. Enacting moral agency involved working in a shifting moral context, and working in-between their own values and those of the organizations in which they worked, in-between their own values and those of others, and in-between competing values and interests. CONCLUSIONS: Analysis of the experiences and concerns of the nurses offered new understanding of ethics in nursing and direction for the development of ethical theory pertinent to nursing practice.
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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 | 0.032 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.075 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".