Influence of stress and nursing leadership on job satisfaction of pediatric intensive care unit nurses
Bibliographic record
Abstract
BACKGROUND: High levels of stress and the challenges of meeting the complex needs of critically ill children and their families can threaten job satisfaction and cause turnover in nurses. OBJECTIVE: To explore the influences of nurses' attributes, unit characteristics, and elements of the work environment on the job satisfaction of nurses in pediatric critical care units and to determine stressors that are unique to nurses working in pediatric critical care. METHOD: A cross-sectional survey design was used. The sample consisted of 1973 staff nurses in pediatric critical care units in 65 institutions in the United States and Canada. The following variables were measured: nurses' perceptions of group cohesion, job stress, nurse-physician collaboration, nursing leadership, professional job satisfaction, and organizational work satisfaction. RESULTS: Significant associations (r = -0.37 to r = -0.56) were found between job stress and group cohesion, professional job satisfaction, nurse-physician collaboration, nursing leadership behaviors, and organizational work satisfaction. Organizational work satisfaction was positively correlated (r = 0.35 to r = 0.56) with group cohesion, professional job satisfaction, nurse-physician collaboration, and nursing leadership behaviors. Job stress, group cohesion, job satisfaction, nurse-physician collaboration, and nursing leadership behaviors explained 52% of the variance in organizational work satisfaction. Dealing with patients' families was the most frequently cited job stressor. CONCLUSIONS: Job stress and nursing leadership are the most influential variables in the explanation of job satisfaction. Retention efforts targeted toward management strategies that empower staff to provide quality care along with focal interventions related to the diminishment of stress caused by nurse-family interactions are warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".