Predictors of nurses considering leaving the profession due to work-related stress in a large pediatric and women’s hospital in the United States
Bibliographic record
Abstract
Nurse retention is of extreme importance in modern healthcare given the ever increasing nursing shortage and the high cost of training newly hired nurses. Research has repeatedly demonstrated that stress is strongly correlated with nursing staff turnover. This study examines the relationship of Secondary Traumatic Stress, Burnout, Compassion Satisfaction, personal life stress and nurse demographic characteristics with having considered leaving the nursing profession due to work-related stress. A survey was administered to nurses at a large pediatric and women’s hospital in the southern United States. Bivariate analyses (n = 496) indicated being Caucasian (p < .001), working fewer hours per week (p = .009), experiencing more personal life stress (p < .001), having higher Burnout (p < .001), or Secondary Traumatic Stress (p < .001) scores or lower Compassion Satisfaction (p = .015) scores were significantly associated with increased likelihood of having considered leaving the nursing profession. In multivariable logistic regression analysis, after variable selection, higher levels of Burnout (p < .001), more life stress (p = .010), being Caucasian (p < .001) and working fewer hours (p = .004) were all significantly associated with higher odds of considering leaving the nursing profession. Interventions to reduce work-related Burnout and help nurses cope with stressful life events are needed to increase retention of nurses in the profession.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".