Moral neutralization: Nurses’ evolution in unethical climate workplaces
Bibliographic record
Abstract
INTRODUCTION: Good quality of care is dependent on nurses' strong clinical skills and moral competencies, as well. While most nurses work with high moral standards, the moral performance of some nurses in some organizations shows a deterioration in their moral sensitivity and actions. The study reported in this paper aimed to explore the experiences of nurses regarding negative changes in their moral practice. MATERIALS AND METHODS: This was a qualitative study utilizing an inductive thematic analysis approach, which was conducted from February 2017 to September 2019. Twenty-five nurses participated in semi-structured interviews. RESULTS: The main theme that emerged from our analysis was one of moral neutralization in the context of an unethical moral climate. We found five sub-themes, including: (1) feeling discouraged; (2) normalization; (3) giving up; (4) becoming a justifier; and (5) moral indifference. CONCLUSIONS: Unethical moral climates in health organizations can result in deterioration of morality in nurses which can harm both patients and health systems. Some unethical behaviors in nurses can be explained by this process.
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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 | 0.015 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".