What is known about nurse retention in peri-COVID-19 and post-COVID-19 work environments: protocol for a scoping review of factors, strategies and interventions
Bibliographic record
Abstract
INTRODUCTION: The pandemic has highlighted a worsening of nurses' working conditions and a global nursing shortage. Little is known about the factors, strategies and interventions that improve nurse retention in the peri-COVID and post-COVID time period. An improved understanding of approaches implemented to support and retain nurses will provide a blueprint for sustaining the nursing workforce. The objectives of this scoping review are to investigate and describe the following: (a) factors associated with nurse retention; (b) strategies suggested to support nurse retention and (c) interventions trialled to support nurse retention, during and after the COVID-19 pandemic. METHODS AND ANALYSIS: Medline, Embase, CINAHL and Scopus will be searched. The included studies will be qualitative, quantitative, mixed methods and grey literature studies of nurses including factors, strategies and/or interventions to support nurse retention in the peri-COVID and post-COVID time period (2019 to present) that are in English or can be translated into English. The excluded studies will be those that focus on nurse managers, educators, students or those in advanced practice roles and studies where the population cannot be segmented to identify which data came from nurses. Systematic, scoping reviews and meta-syntheses will be excluded, but their reference lists will be hand-screened for suitable studies. Data will be evaluated for quality and synthesised qualitatively to map the current evidence available. The relevant studies will be reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. ETHICS AND DISSEMINATION: Approval for the broader research study, including this scoping review, has been obtained from the university health sciences research board (protocol #00042510). All data for this scoping review will be collected from published literature, and findings will be published in a peer-reviewed journal and presented at relevant conferences. TRIAL REGISTRATION NUMBER: The protocol was registered on Open Science Framework (4 April 2024) https://doi.org/10.17605/OSF.IO/XWH45.
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 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 | 0.099 | 0.106 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.018 | 0.022 |
| Bibliometrics | 0.025 | 0.020 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.066 | 0.014 |
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".