Emergency Nurses’ Perceptions of Leadership Strategies and Intention to Leave: A scoping review of the literature
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
BackgroundRetention of registered nurses in emergency departments (EDs) is as a critical issue, further exacerbated by the COVID pandemic. Leaders influence work life and working environment, but it is unclear what strategies leaders use to address nurse staffing issues in the ED. The purpose of this scoping review is to understand if leadership strategies used in EDs have links to nursing retention and turnover. MethodologyThis scoping review was completed with a comprehensive search within Cumulative Index to Nursing and Allied Health Literature, EMCARE, EMBASE. Two authors developed inclusion and exclusion criteria, did title and abstract screening, and full text screening using review software. Data extracted from included studies was analyzed to determine leadership strategies and relationships to intent to stay, retention, intent to leave, or turnover. ResultsOf the 553 records identified, nine met inclusion criteria. Leadership strategies identified in the studies included support from supervisor, engagement by the leader, organizational culture assessment, and a cultural change toolkit. No leadership strategy influenced nurse intention to stay, retention, intention to leave or turnover. ConclusionEmergency nurse retention and the prevention of turnover is a multidimensional issue stemming from various factors that may not be controllable due to the nature of the setting. However, leaders can implement strategies and provide support to staff to enhance quality of work life and the work environment. More information is needed to understand how leaders can influence the current and future supply of emergency nurses to produce quality patient care outcomes.
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it