Moral Distress Scores of Nurses Working in Intensive Care Units for Adults Using Corley’s Scale: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: No systematic review in the literature has analyzed the intensity and frequency of moral distress among ICU nurses. No study seems to have mapped the leading personal and professional characteristics associated with high levels of moral distress. This systematic review aimed to describe the intensity and frequency of moral distress experienced by nurses in ICUs, as assessed by Corley's instruments on moral distress (the Moral Distress Scale and the Moral Distress Scale-Revised). Additionally, this systematic review aimed to summarize the correlates of moral distress. METHODS: A systematic search and review were performed using the following databases: Cumulative Index to Nursing and Allied Health Literature (CINAHL), the National Library of Medicine (MEDLINE/PubMed), and Psychological Abstracts Information Services (PsycINFO). The review methodology followed PRISMA guidelines. The quality assessment of the included studies was conducted using the Newcastle-Ottawa Scale. RESULTS: Findings showed a moderate level of moral distress among nurses working in ICUs. The findings of this systematic review confirm that there are a lot of triggers of moral distress related to patient-level factors, unit/team-level factors, or system-level causes. Beyond the triggers of moral distress, this systematic review showed some correlates of moral distress: those nurses working in ICUs with less work experience and those who are younger, female, and intend to leave their jobs have higher levels of moral distress. This systematic review's findings show a positive correlation between professional autonomy, empowerment, and moral distress scores. Additionally, nurses who feel supported by head nurses report lower moral distress scores. CONCLUSIONS: This review could help better identify which professionals are at a higher risk of experiencing moral distress, allowing the early detection of those at risk of moral distress, and giving the organization some tools to implement preventive strategies.
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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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".