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护理教育中数字健康领域被引用次数最多的前100项年度研究的文献计量学洞察(2020 - 2024年)

Bibliometric insights into the top 100 most-cited annual studies on digital health in nursing education (2020-2024).

作者信息

Zhou Chunxiu, Ma Lili

机构信息

Precision Health Management Center, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China.

Department of Nursing, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China.

出版信息

Digit Health. 2025 May 14;11:20552076251342165. doi: 10.1177/20552076251342165. eCollection 2025 Jan-Dec.

DOI:10.1177/20552076251342165
PMID:40376573
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12078968/
Abstract

AIM

This study aims to use bibliometric methods to analyze highly cited nursing education articles on digital health, particularly those 100 top cited publications between 2020 and 2024, from the Web of Science (WOS) Core Collection.

DESIGN

A retrospective bibliometric analysis was conducted.

METHODS

A bibliometric analysis of the most-cited digital health articles on nursing education in English with the highest citations. Data were sourced from the WOS Core Collection. Analysis was conducted using Excel and SPSS, while VOSviewer was used to visualize keyword trends. The analysis included examining journal distribution, author patterns, research types, methodologies, and keyword trends.

RESULTS

The 100 most-cited articles were published between 1993 and 2024 by 91 different first authors from 22 countries. The United States contributed approximately 33% of the articles. Citation counts ranged from 198 to 1. Nurse Education Today was the most frequently cited journal. Review articles had higher average citations (50.96) compared to original articles (24.08). The focus of research has shifted from virtual reality (VR) to artificial intelligence (AI), with ChatGPT emerging as a new trend.

CONCLUSION

Digital health is becoming a significant focus in nursing education research. While VR has been a dominant topic, AI is now emerging as a key research area. The findings provide insights into citation patterns and research trends, supporting future impactful studies in this field.

摘要

目的

本研究旨在运用文献计量学方法,分析来自科学网(WOS)核心合集的数字健康领域高被引护理教育文章,特别是2020年至2024年间被引次数排名前100的出版物。

设计

进行了一项回顾性文献计量分析。

方法

对英语语言中被引次数最多的护理教育数字健康文章进行文献计量分析。数据来源于WOS核心合集。使用Excel和SPSS进行分析,同时使用VOSviewer可视化关键词趋势。分析内容包括考察期刊分布、作者模式、研究类型、方法以及关键词趋势。

结果

这100篇被引次数最多的文章于1993年至2024年间由来自22个国家的91位不同的第一作者发表。美国贡献了约33%的文章。被引次数从198次到1次不等。《今日护理教育》是被引频率最高的期刊。综述文章的平均被引次数(50.96)高于原创文章(24.08)。研究重点已从虚拟现实(VR)转向人工智能(AI),ChatGPT成为新趋势。

结论

数字健康正成为护理教育研究的一个重要焦点。虽然VR一直是主导话题,但AI现在正成为一个关键研究领域。这些发现为引文模式和研究趋势提供了见解,支持了该领域未来有影响力的研究。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3e7/12078968/f83ac4812494/10.1177_20552076251342165-fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3e7/12078968/3a50e7122f1a/10.1177_20552076251342165-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3e7/12078968/573149370a3e/10.1177_20552076251342165-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3e7/12078968/07f8e46c68ae/10.1177_20552076251342165-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3e7/12078968/52c3ccd56c78/10.1177_20552076251342165-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3e7/12078968/f83ac4812494/10.1177_20552076251342165-fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3e7/12078968/3a50e7122f1a/10.1177_20552076251342165-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3e7/12078968/573149370a3e/10.1177_20552076251342165-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3e7/12078968/07f8e46c68ae/10.1177_20552076251342165-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3e7/12078968/52c3ccd56c78/10.1177_20552076251342165-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3e7/12078968/f83ac4812494/10.1177_20552076251342165-fig5.jpg

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