Exploring the key predictors of retention in emergency nurses
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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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".