Alarm fatigue and moral distress in ICU nurses in COVID-19 pandemic
Bibliographic record
Abstract
INTRODUCTION: Most ICU nurses feel overwhelmed by the variety of alarms at the same time. Therefore, nurses experience very stressful situations in relation to many responsibilities and care demands. This stressful condition has recently been exacerbated by COVID-19 and potentially endangers patient safety. The aim of this study was to investigate the alarm fatigue and moral distress of ICU nurses in COVID-19 crisis. METHOD: This is a descriptive-analytical cross-sectional study (April-May 2021). Sampling was done by convenience among ICU nurses affiliated to Isfahan University of Medical Sciences, Iran. Data were collected using Nurses' alarm fatigue and the moral distress scale (MDS). Data were analyzed using ANOVA, independent t-test and multivariate logistic regression. RESULT: The results showed that the mean score of alarm fatigue was moderate)19.08 ± 6.26 (and moral distress was low (33.80 ± 11.60). The results showed that there was a significant relationship between alarm fatigue and related training courses)P = .012(.So that, alarm fatigue in nurses who were trained in working with ventilators and alarm settings was significantly less than other nurses. Also, a significant relationship was found between moral distress and marital status(P = .001) and Shift type(P = .01). On the other hand, the risk of alarm fatigue was higher in participants who have a PhD. The results showed that no significant correlation was found between alarm fatigue and moral distress (r = 0.111, P = 0.195). CONCLUSION: It is suggested that practical training courses on alarm management be included in the curriculum and the ICU nurses should have practical training before starting work in the ICU and on an annual basis. In order to protect nurses and ensure quality care of patients, nurse managers should reduce the number of rotating shifts of ICU nurses.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".