Bibliographic record
Abstract
Nurses may, and often do, experience moral distress in their careers. This is related to the complicated work environment and the complex nature of ethical situations in everyday nursing practice. The outcomes of moral distress may include psychological and physical symptoms, reduced job satisfaction and even inadequate or inappropriate nursing care. Moral distress can also impact retention of nurses. Although research has grown considerably over the past few decades, there is still a great deal about this topic that we do not know including how to deal well with moral distress. A critical key step is to develop a deeper understanding of relational practice as it pertains to moral distress. In this article, exploration of the experience of moral distress among nurses is guided by the key elements of relational ethics. This ethical approach was chosen because it recognizes that ethical practice is situated in relationships and it acknowledges the importance of the broader environment on influencing ethical action. The findings from this theoretical exploration will provide a theoretical foundation upon which to advance our knowledge about moral distress.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.046 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.006 |
| 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".