Nurse retention in peri- and post-COVID-19 work environments: a scoping review of factors, strategies and interventions
Bibliographic record
Abstract
OBJECTIVES: The COVID-19 pandemic highlighted the deterioration of nurses' working conditions and a growing global nursing shortage. Little is known about the factors, strategies and interventions that could improve nurse retention in the peri- and post-COVID-19 period. An improved understanding of strategies that support and retain nurses will provide a foundation for developing informed approaches to sustaining the nursing workforce. The aim of this scoping review is to investigate and describe the (1) factors associated with nurse retention, (2) strategies to support nurse retention and (3) interventions that have been tested to support nurse retention, during and after the COVID-19 pandemic. DESIGN: Scoping review. DATA SOURCES: This scoping review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews. MEDLINE, Embase, CINAHL and Scopus databases were searched on 17 April 2024. The search was limited to a publication date of '2019 to present'. ELIGIBILITY CRITERIA: Qualitative, quantitative, mixed-methods and grey literature studies of nurses (Registered Nurse (RN), Licenced Practical Nurse (LPN), Registered Practical Nurse (RPN), Publlic Health Nurse (PHN), including factors, strategies and/or interventions to support nurse retention in the peri- and post-COVID-19 period in English (or translated into English), were included. Systematic reviews, scoping reviews and meta-syntheses were excluded, but their reference lists were hand-screened for suitable studies. DATA EXTRACTION AND SYNTHESIS: The following data items were extracted: title, journal, authors, year of publication, country of publication, setting, population (n=), factors that mitigate intent to leave (or other retention measure), strategies to address nurse retention, interventions that address nurse retention, tools that measure retention/turnover intention, retention rates and/or scores. Data were evaluated for quality and synthesised qualitatively to map the current available evidence. RESULTS: Our search identified 130 studies for inclusion in the analysis. The majority measured some aspect of nurse retention. A number of factors were identified as impacting nurse retention including nurse demographics, safe staffing and work environments, psychological well-being and COVID-19-specific impacts. Nurse retention strategies included ensuring safe flexible staffing and quality work environments, enhancing organisational mental health and wellness supports, improved leadership and communication, more professional development and mentorship opportunities, and better compensation and incentives. Only nine interventions that address nurse retention were identified. CONCLUSIONS: Given the importance of nurse retention for a variety of key outcomes, it is imperative that nursing leadership, healthcare organisations and governments work to develop and test interventions that address nurse retention.
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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 | 0.023 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 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".