Factors affecting family-centered care practice by nurses: A systematic review

Purpose: This study presents a systematic review of the obstacles to and enablers of family-centered care (FCC) implementation by nurses. FCC, which has demonstrated beneficial outcomes, is regarded as a crucial quality measure in certain pediatric units. However, not all nurses incorporate FCC into their practice. Design and methods: A systematic review was conducted from January to June 2023, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Various medical subject heading keywords and terms were used to search electronic databases, with the aim of synthesizing and evaluating the results. Results: Twenty-three articles were identified for review. Most of these studies were carried out in Western countries. They revealed several facilitators and obstacles to FCC implementation by nurses when caring for sick children. Some of these factors are linked to the personal attributes of the nurses, while others are associated with the characteristics of the families and the healthcare system itself. Conclusion: Nurses face multi-level barriers that hinder their ability to implement FCC practice. This systematic review identifies the need to leverage nurses' attributes, foster effective nurse-client relationships, and promote organizational changes. Practice implications: Nurses need to comprehend and work toward altering the factors that influence the delivery of FCC. The findings of this review can be used by healthcare organization leaders and policymakers to customize interventions and allocate resources to promote FCC practice. Further research in diverse cultural contexts is needed to examine the cause-and-effect relationship concerning the influence of the identified barriers and facilitators on FCC practice. In addition, experimental studies are required to evaluate the effectiveness of evidence-based interventions on FCC practice by nurses. (c) 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.