Performance analysis and science mapping of family resilience research in the context of children and adolescents: A bibliometric study


DEMİRCİOĞLU H., Demir E.

JOURNAL OF PEDIATRIC NURSING-NURSING CARE OF CHILDREN & FAMILIES, cilt.80, ss.100-107, 2025 (SCI-Expanded) identifier identifier identifier

Özet

Problem: The present study explores research on family resilience in the context of children and adolescents, as well as studies covering all family members, and examines the conceptual, intellectual, and social structures of the research through bibliometric analysis. Materials and methods: We carried out the search procedure on the abstracts of papers indexed to Social Science Citation Index (SSCI), Science Citation Index Expanded (SCI-E), Emerging Sources Citation Index (ESCI), and Arts & Humanities Citation Index (AHCI) within the Web of Science (WoS) database. Results: We could access 407 papers since 1994 pertaining to the topic of family resilience in the context of children and adolescents. These records are distributed across 245 sources, and we calculated the annual growth rate of the research to be 14.49. The United States of America seems to lead the research field, followed by China and Canada. Moreover, we present findings regarding the conceptual, intellectual, and social structures of the selected research. Conclusion: We conclude that family resilience is an important issue in the context of children and adolescents. In this sense, our findings would provide a foundation for further investigation into family resilience in the mentioned context. Finally, we anticipate that our results will contribute to evidence-based policies concerning family resilience in the context of children and adolescents. Practical implications: The study offers a comprehensive view of family resilience in the context of children and adolescents, particularly in the domain of health. Furthermore, it provides insights into bibliometric analysis. (c) 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.