Moral distress in critical care nursing practice: A concept analysis
Bibliographic record
Abstract
AIM: To provide a critical analysis of the concept of moral distress (MD) in critical care (CC) nursing. BACKGROUND: Despite extensive inquiry pertaining to the legitimacy of MD within nursing discourse, some authors still question its relevancy to the profession. However, amid the global COVID-19 pandemic, MD is generating a significant amount of discussion anew, warranting the further exploration of the concept within CC nursing to provide clarity and expand on the definition. DESIGN: Rodger's Evolutionary Concept Analysis method was used to guide this analysis. METHODS: Related terms, attributes, antecedents, and consequences of MD were identified using current literature. RESULTS: The results of this analysis demonstrate strong congruence between the attributes, antecedents, and negative consequences pertaining to MD. However, a new theme has emerged from this review of the contemporary literature, highlighting the potential unexpected positive outcomes perceived by nurses who experience MD, including the provision of better care, increased levels of empathy, and enhanced opportunities for ethical reflection.
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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 | 0.021 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".