The effectiveness of intervention strategies to improve nurse retention
Bibliographic record
Abstract
Background and aim: Nurse retention is a persistent issue in the global health sector. Nurses are essential to the strength and resilience of healthcare systems, but current supply, demand, and the needs of the population lead to threats that undermine universal healthcare goals. Consideration of strategies to improve nurse turnout in hospitals has become crucial to national and global healthcare systems. The aim of the study was to identify the effectiveness of intervention strategies implemented to improve nurse retention in 2020 at King Abdullah Medical Complex in Jeddah (KAMCJ).Methods: The present study reviewed the intervention strategies that were carried out in 2020 at KAMCJ in order to improve nurse retention, 511 nurses were included in the improvement project and underwent yearly satisfaction surveys and exit interviews in 2020. The information from the available documents was gathered retrospectively. Results: The current study's findings indicated that the nurse satisfaction and exit interview results were positive in terms of the nurse's working environment, professional development, and promotion opportunities. According to the outcomes, the turnover rate decreased from 19.70% in 2019 to 8.90% in 2020, while the retention rate increased from 80.30% in 2019 to 91.10% in 2022.Conclusions: This study highlighted the importance of developing a multi-dimensional strategy to address issues related to nursing job satisfaction, such as professional growth and development and nurses' working conditions, which significantly increase nursing retention.
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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 | 0.011 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".