Big data research in nursing: A bibliometric exploration of themes and publications

IF 2.4 3区 医学 Q1 NURSING
Bo Li, Kun Du, Guanchen Qu, Naifu Tang
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引用次数: 0

Abstract

Aims

To comprehend the current research hotspots and emerging trends in big data research within the global nursing domain.

Design

Bibliometric analysis.

Methods

The quality articles for analysis indexed by the science core collection were obtained from the Web of Science database as of February 10, 2023.The descriptive, visual analysis and text mining were realized by CiteSpace and VOSviewer.

Results

The research on big data in the nursing field has experienced steady growth over the past decade. A total of 45 core authors and 17 core journals around the world have contributed to this field. The author's keyword analysis has revealed five distinct clusters of research focus. These encompass machine/deep learning and artificial intelligence, natural language processing, big data analytics and data science, IoT and cloud computing, and the development of prediction models through data mining. Furthermore, a comparative examination was conducted with data spanning from 1980 to 2016, and an extended analysis was performed covering the years from 1980 to 2019. This bibliometric mapping comparison allowed for the identification of prevailing research trends and the pinpointing of potential future research hotspots within the field.

Conclusions

The fusion of data mining and nursing research has steadily advanced and become more refined over time. Technologically, it has expanded from initial natural language processing to encompass machine learning, deep learning, artificial intelligence, and data mining approach that amalgamates multiple technologies. Professionally, it has progressed from addressing patient safety and pressure ulcers to encompassing chronic diseases, critical care, emergency response, community and nursing home settings, and specific diseases (Cardiovascular diseases, diabetes, stroke, etc.). The convergence of IoT, cloud computing, fog computing, and big data processing has opened new avenues for research in geriatric nursing management and community care. However, a global imbalance exists in utilizing big data in nursing research, emphasizing the need to enhance data science literacy among clinical staff worldwide to advance this field.

Clinical Relevance

This study focused on the thematic trends and evolution of research on the big data in nursing research. Moreover, this study may contribute to the understanding of researchers, journals, and countries around the world and generate the possible collaborations of them to promote the development of big data in nursing science.

护理领域的大数据研究:对主题和出版物的文献计量学探索。
目的:了解当前全球护理领域大数据研究的研究热点和新兴趋势:文献计量分析:通过CiteSpace和VOSviewer实现描述性分析、可视化分析和文本挖掘:近十年来,护理领域的大数据研究经历了稳步增长。全球共有 45 位核心作者和 17 种核心期刊为这一领域做出了贡献。作者通过关键词分析发现了五个不同的研究重点集群。它们包括机器/深度学习和人工智能、自然语言处理、大数据分析和数据科学、物联网和云计算,以及通过数据挖掘开发预测模型。此外,还对1980年至2016年的数据进行了比较研究,并对1980年至2019年的数据进行了扩展分析。通过这种文献图谱比较,可以确定当前的研究趋势,并指出该领域未来潜在的研究热点:随着时间的推移,数据挖掘与护理研究的融合稳步发展,并变得更加完善。在技术上,它已从最初的自然语言处理扩展到机器学习、深度学习、人工智能以及融合多种技术的数据挖掘方法。在专业方面,它已从解决患者安全和压疮问题发展到涵盖慢性疾病、重症监护、应急响应、社区和养老院环境以及特定疾病(心血管疾病、糖尿病、中风等)。物联网、云计算、雾计算和大数据处理的融合为老年护理管理和社区护理研究开辟了新途径。然而,在护理研究中利用大数据方面存在着全球性的不平衡,强调需要提高全球临床工作人员的数据科学素养,以推动这一领域的发展:本研究关注护理研究中大数据的主题趋势和研究演变。此外,本研究还有助于世界各国的研究人员、期刊和国家对大数据的了解,并产生可能的合作,以促进护理科学大数据的发展。
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来源期刊
CiteScore
6.30
自引率
5.90%
发文量
85
审稿时长
6-12 weeks
期刊介绍: This widely read and respected journal features peer-reviewed, thought-provoking articles representing research by some of the world’s leading nurse researchers. Reaching health professionals, faculty and students in 103 countries, the Journal of Nursing Scholarship is focused on health of people throughout the world. It is the official journal of Sigma Theta Tau International and it reflects the society’s dedication to providing the tools necessary to improve nursing care around the world.
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