Listening to Nurses' Moral Voices: Building a Quality Health Care Environment
Bibliographic record
Abstract
In this paper we describe a research project in nursing ethics aimed at exploring the meaning of ethics for nurses providing direct care with clients. This was a practice-based project in which participants who were staff nurses, nurses in advanced practice, and students in nursing were asked to tell us (or describe to us) how they thought about ethics in their practice, and what ethical practice meant to them. We then undertook to analyze, describe and understand the enactment of ethical practice, the opportunities for and barriers to such enactment, as well as the resources nurses need for ethical practice. We drew out implications of these findings for nursing leaders. We identified practice realities that create a climate for ethical or moral distress, and the way in which nurses attempt to maintain their moral agency. Practice realities included nurses' ethical concerns about policies guiding care; the financial, human and temporal resources available for care; and the power and conflicting loyalties nurses encounter inproviding good care. Maintaining moral agency involved use of a variety of ethical resources and the identification of resources needed to provide good care, as well as the processes used to enact moral agency. Nurse leaders are also moral agents. Important implications of these findings for nursing leaders are that they need moral courage to be self-reflective, to name their own moral distress, and to act so that their nursing staff are able to be moral agents. Nurse leaders need to be the moral compass for nurses, using their power as a positive force to promote, provide and sustain quality practice environments for safe, competent and ethical 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 | 0.064 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.031 | 0.049 |
| Scholarly communication | 0.040 | 0.022 |
| Open science | 0.005 | 0.041 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".