Understanding the factors which promote registered nurses’ intent to stay in emergency and critical care areas
Bibliographic record
Abstract
AIMS AND OBJECTIVE: To explore the influential factors and strategies that promote an experienced nurse's intent to stay in their emergency or critical care area. BACKGROUND: Turnover among registered nurses (herein referred to as nurses) working in specialty areas of practice can result in a range of negative outcomes. The retention of specialty nurses at the unit level has important implications for hospital and health systems. These implications include lost knowledge and experience which may in turn impact staff performance levels, patient outcomes, hiring, orientating, development of clinical competence and other aspects of organizational performance. DESIGN: This qualitative study used an interpretive descriptive design to understand nurses' perceptions of the current factors and strategies that promote them staying in emergency or critical care settings for two or more years. METHODS: Focus groups were conducted with 13 emergency and critical care nurses. Data analysis involved thematic analysis that evolved from codes to categories to themes. RESULTS: Four themes were identified: leadership, interprofessional relationships, job fit and practice environment. In addition, the ideas of feeling valued, respected and acknowledged were woven throughout. CONCLUSIONS: Factors often associated with nurse attrition such as burnout and job stresses were not emphasised by the respondents in our study as critical to their intent to stay in their area of practice. This study has highlighted positive aspects that motivate nurses to stay in their specialty areas. RELEVANCE TO CLINICAL PRACTICE: To ensure quality care for patients, retention of experienced emergency and critical care nurses is essential to maintaining specialty expertise in these practice settings.
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.
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 | 0.001 | 0.007 |
| 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 | 0.001 |
| 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".