Estimation of moral distress among nurses: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Moral distress is a common challenge among professional nurses when caring for their patients, especially when they need to make rapid decisions. Therefore, leaving moral distress unconsidered may jeopardize patient quality of care, safety, and satisfaction. AIM: To estimate moral distress among nurses. METHODS: This systematic review and meta-analysis conducted systematic search in Scopus, PubMed, ProQuest, ISI Web of Knowledge, and PsycInfo up to end of February 2022. Methodological quality of included studies was assessed using the Newcastle Ottawa checklist. Data from included studies were pooled by meta-analysis with random effect model in STATA software version 14. The selected key measure was mean score of moral distress total score with its' 95% Confidence Interval was reported. Subgroup analyses and meta-regressions were conducted to identify possible sources of heterogeneity and potentially influencing variables on moral distress. Funnel plots and Begg's Tests were used to assess publication bias. The Jackknife method was used for sensitivity analysis. ETHICAL CONSIDERATION: The protocol of this project was registered in the PROSPERO database under decree code of CRD42021267773. RESULTS: :0.94]. Publication bias and small study effect was ruled out. Moral distress significantly decreased in the COVID-19 pandemic versus before. Nurses working in developing countries experienced higher level of moral distress compared to their counterparts in developed countries. Nurses' workplace (e.g., hospital ward) was not linked to severity of moral disturbance. CONCLUSION: The results of the study showed a low level of pooled estimated score for moral distress. Although the score of moral distress was not high, nurses working in developing countries reported higher levels of moral distress than those working in developed countries. Therefore, it is necessary that future studies focus on creating a supportive environment in hospitals and medical centers for nurses to reduce moral distress and improve healthcare.
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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 | 0.024 | 0.055 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.042 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".