Global prevalence of turnover intention among intensive care nurses: A meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Nurse turnover is considered a major cause of nurse shortage, representing problems for health care systems in terms of both quality and cost of care for patients, and intention to leave is the strongest practical predictor variable of actual turnover. AIM: This systematic review and meta-analysis aims at exploring the global prevalence of turnover intention in intensive care nurses. DESIGN: This was a systematic literature review. METHODS: A systematic review of empirical quantitative studies on turnover intention in nurses of intensive care units (ICUs), published in English till March 2021, was conducted. The databases PubMed, Embase, ISI Web of Knowledge, and CINAHL were searched. Eligible studies were observational or descriptive studies that reported the prevalence of turnover intention among nurses in all types of ICUs. The quality of studies was assessed using a modified Newcastle-Ottawa Scale. A random effect meta-analysis was conducted to estimate the pooled prevalence of turnover intention among ICU nurses. RESULTS: We identified 18 cross-sectional studies investigating a total of 23 140 intensive care nurses from 23 countries. The intention to leave rate was ranged from 3.0% to 75.0%. The pooled prevalence of turnover intention was 27.7% (95% confidence interval: 21.6%-34.3%). CONCLUSIONS: This meta-analysis showed that more than 27% of the intensive care nurses had the intention to leave worldwide. In the current context of nursing shortage, efforts should be made to improve conditions for this important group of care providers. RELEVANCE TO CLINICAL PRACTICE: The prevalence of turnover intention is relatively high among intensive care nurses. Nurse managers should take this intention seriously, as the intention to leave may lead to an actual decision to leave the profession.
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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 | 0.019 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.060 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".