自2000年以来在医学期刊上发表论文的10位最受引用的作者,使用中间中心来识别独特的名字:图书法分析分析:
Chi-Sheng Chang1,2, Julie Chi Chow3,4, Tsair-Wei Chien5
1Center for Quality Management, Chi Mei Medical Center, Liouying, Tainan, Taiwan.
Medicine
|June 23, 2023
概括
本研究引入了作者权重方案 (AWS) 和中间中心性 (BC) 以确定最多引用的作者及其研究主题,克服了在文献计量分析中重复名称等挑战.
科学领域:
- 图书识别和科学识别技术
- 信息科学 信息科学
- 医学研究分析 医学研究分析
背景情况:
- 识别科学领域有影响力的作者至关重要,但由于相同的作者名称和不同的共同作者贡献而受到挑战.
- 如果不解决这些问题,以前的文献计量研究可能会产生偏见的结果.
- 准确识别最多引用的作者需要强大的方法.
研究的目的:
- 展示作者加权方案 (AWS) 和中间中心性 (BC) 的应用,以确定特定期刊中最常被引用的前10位作者.
- 分析这些引用最多的作者的主要研究主题.
- 为了应对作者歧义和文献计量分析中的贡献评估方面的挑战.
主要方法:
- 从PubMed (2000-2020) 收集了24,058个摘要,使用关键字"医学[期刊]".
- 应用AWS来确定具有更高x指数的作者和BC来解决名称明确性问题.
- 利用社交网络分析 (SNA) 来识别作者集群,并分析医学科目标题 (MeSH术语) 来识别研究主题.
主要成果:
- 确定了十大最常被引用的作者,并指出了两个名字相同的中国作家 (Jing Li和Tao-Wang).
- 美国的JA Winkelstein获得了最高的x指数 (15.58),而台湾的Chia-Hung Kao则是最多产的作者,有115篇文章.
- 通过对顶级作者作品的MeSH术语分析,确定了三个主要研究主题:并发症,病因学和流行病学.
结论:
- 作者加权计划 (AWS) 和中间中心性 (BC) 有效地识别了引用最多的作者及其研究主题.
- 应用的方法 (BC和AWS) 为未来的文献计量分析提供了可靠的方法.
- 这项研究为准确的作者识别和学术领域的研究趋势分析提供了一个框架.
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