Nurse retention in peri- and post-COVID-19 work environments: a scoping review of factors, strategies and interventions
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,023 | 0,073 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,008 |
| Bibliométrie | 0,012 | 0,012 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».