Exploring the key predictors of retention in emergency nurses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AIM: To explore the factors that predict the retention of nurses working in emergency departments. BACKGROUND: The escalating shortage of nurses is one of the most critical issues facing specialty areas, such as the emergency department. Therefore, it is important to identify the key influencing and intermediary factors that affect emergency department nurses' intention to leave. METHODS: As part of a larger study, a cross-sectional survey was completed by 261 registered nurses working in the 12 designated emergency departments within rural, urban community and tertiary hospitals in Manitoba, Canada. RESULTS: Twenty-six per cent of the respondents will probably/definitely leave their current emergency department jobs within the next year. Engagement was the key predictor of intention to leave (P < 0.001). Engagement was also associated with job satisfaction, compassion satisfaction, compassion fatigue, and burnout (P < 0.05). In an ordinal least-squares model (R(2) = 0.44), nursing management, professional practice, collaboration with physicians, staffing resources and shift work emerged as significant influencing factors for engagement. CONCLUSIONS: Engagement plays a central role in emergency department nurses intention to leave. Addressing the factors that influence engagement may reduce emergency department nurses' intention to leave. IMPLICATIONS FOR NURSING MANAGEMENT: This study highlights the value of research-based evidence as the foundation for developing innovative strategies for the retention of emergency department nurses.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 it