Moral distress among health care workers in the intensive care unit; a systematic review and meta-analysis
Bibliographic record
Abstract
Background: The effect of moral distress among healthcare providers is significant on disease morbidity, especially within the intensive care unit (ICU). In this systematic review and meta-analysis, we aimed to gather all evidence regarding moral distress frequency and severity/intensity among ICU health care providers. Methods: We conducted a systematic search to gather all relevant studies from six databases, followed by a manual search of references. Fourteen studies consisting of 5905 participants were included in the final moral distress scale analyses. Results: Overall, there was moderate moral distress severity/intensity among all participants (Mean = 27.79; 95% confidence interval (CI) = 7.40–64.18). On further stratification of the results according to countries, Canada (Mean = 91.99; 95% CI = 80.10–105.65) and USA (Mean = 52.54; 95% CI = 44.78–61.64) showed the highest distress scores, followed by Iran (Mean = 21.20; 95% CI = 7.21–62.30) and Italy (Mean = 3.42; 95% CI = 3.15–3.72). Studies conducted in high income-earning countries reported more severity/intensity (Mean = 22.65; 95% CI = 6.58–78.02) compared to those in the upper-middle income-earning ones (Mean = 18.89; 95% CI = 2.80–127.34). There was significant heterogeneity among the included studies, which could not be explained by the difference in scales, country of the participants, or the female proportion. Moreover, there was a moderate frequency of moral distress (Mean = 46.83; 95% CI = 8.34–262.87), which was found to be much higher (Mean = 87.94; 95% CI = 83.55–92.57), in performing analysis. Conclusion: Moral distress is a major problem in the ICU setting, in terms of both severity/intensity and frequency. Future large-scale studies are required, through a unified framework, to develop appropriate interventions to address ICU-related moral distress.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.002 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.010 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".