PP348 Topic: AS11–Healthcare Systems/Economic Evaluation/Capacity Building/Administration/Other: NURSE RETENTION IN PEDIATRIC CRITICAL CARE: IMPACTS OVERTIME AND REDEPLOYMENT ON NURSE INTENT TO LEAVE
Bibliographic record
Abstract
Aims & Objectives: Nurse attrition is an important and growing concern in pediatric critical care (PCC). Increased hiring opportunities have resulted in increased job mobility and trends to higher turnover in PCC nurses. Understanding frontline nurses’ perceptions of their work life and job satisfaction is a key step in addressing attrition in this workforce. We describe a single quaternary hospital study of the impacts of overtime and redeployment initiatives on job satisfaction and intent to leave in bedside PCC nurses. Methods: Electronic self-report surveys were sent to all PCC nurses. Data was collected via REDCap software and submitted anonymously online in January 2024 for descriptive statistical analysis. The survey was approved through the organization’s quality improvement process. Results: Forty-one percent (n=137) of eligible nurses completed the survey. Seventy-nine percent (n=108) reported they considered leaving their PCC bedside position in the last year. Less than 10% (n=11) felt that paid compensation was matched to their job demands. Participants reported they felt valued by peers and managers while acknowledging limited recognition for their specialty skills at the organizational level. Critical care premiums, scheduling benefits and improved workload ranked as the highest incentives for retention in this sample. Conclusions: Pediatric critical care nurses are at risk of job dissatisfaction and attrition. Creative compensation packages for nurses with specialty skill sets is an opportunity for organizations and nursing leaders to address nurse staffing shortfalls in this high demand area. Keywords: retention, nursing
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.236 | 0.061 |
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".