The Effect of Problem Solving Skills Training on Moral Distress of Neonatal Intensive Care Unit Nurses
Bibliographic record
Abstract
Introduction: Moral distress is one of the ethical challenges in the nursing profession. Nurses in the neonatal ward are at risk of moral distress most commonly results from disproportionate interventions perceived to not be in the child’s best interests. The aim of this study was to determine the effect of problem-solving skills training on the moral distress of neonatal intensive care unit nurses. Methods: This quasi-experimental study performed on70 nurses working in neonatal intensive care units at Pediatric Medical Center affiliated Tehran University of Medical Sciences from December 2019-June 2020. They were assigned randomly in two equal groups. Corley Ethical Distress Scale Corley was completed before and four weeks after the intervention in two groups. Problem-solving skills training was conducted in (6 sessions of 1.5 hours (2 sessions per week) as a group for the intervention group. Data were analyzed using the software SPSS 22 and tests, chi-square, Fisher's exact, t-test, and paired t-test with significance. 0.5% . Results: The results of comparing the moral distress of nurses working in the neonatal intensive care unit showed that before the intervention, the score of moral distress in the intervention group (150.07 ± 6.87) and in the control group (146.05±6.36) Although before the intervention, the moral distress score of the nurses in the test group was significantly higher than the intervention group P=0.013, but 4 months after the intervention, this score decreased significantly (P <0 01). Conclusion: According to the findings of this study, problem-solving skills improve moral distress in nurses. Therefore, due to the destructive effects of moral distress on the quality of nurses' work, it is suggested that nursing managers develop programs to improve problem solving skills in 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".