Neonatal Intensive Care Unit Nurses Working in an Open Ward
Bibliographic record
Abstract
There is some research on the impact of open-ward unit design on the health of babies and the stress experienced by parents and nurses in neonatal intensive care units. However, few studies have explored the factors associated with nurse stress and work satisfaction among nurses practicing in open-ward neonatal intensive care units. The purpose of this study was to examine what factors are associated with nurse stress and work satisfaction among nurses practicing in an open-ward neonatal intensive care unit. A cross-sectional correlational design was used in this study. Participants were nurses employed in a 34-bed open-ward neonatal intensive care unit in a major university-affiliated hospital in Montréal, Quebec, Canada. A total of 94 nurses were eligible, and 86 completed questionnaires (91% response rate). Descriptive statistics were computed to describe the participants' characteristics. To identify factors associated with nurse stress and work satisfaction, correlational analysis and multiple regression analyses were performed with the Nurse Stress Scale and the Global Work Satisfaction scores as the dependent variables. Different factors predict neonatal intensive care unit nurses' stress and job satisfaction, including support, family-centered care, performance obstacles, work schedule, education, and employment status. In order to provide neonatal intensive care units nurses with a supportive environment, managers can provide direct social support to nurses and influence the culture around teamwork.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".