在研究中识别作者角色:基于卡诺模型的文献计量分析,用于医学杂志 (巴尔的摩) 2023年
Willy Chou1,2, Julie Chi Chow3,4
1Department of Physical Medicine and Rehabilitation, Chiali Chi-Mei Hospital, Tainan, Taiwan.
Medicine
|August 30, 2024
概括
这项研究应用了Kano模型来分析医学研究中的作者角色,揭示了超越传统层次结构的复杂贡献. 调查结果证实中国是2023年医学杂志 (巴尔的摩) 的研究领导者,美国是合作者.
科学领域:
- 图书识别和科学识别技术
- 医学研究分析 医学研究分析
- 学术传播学术交流
背景情况:
- 在学术期刊的作者贡献往往过于简单化成线性层次结构.
- 通常用于客户满意度的Kano模型提供了一个新的框架来理解多方面的作者角色.
- 现有的研究缺乏对医学出版物中作者角色的细微分类.
研究的目的:
- 应用Kano模型来剖析和分类医学研究出版物中的作者角色.
- 测试中国作为研究领导者和美国作为研究合作者的假设.
- 在作者身份上超越传统的领导和支持二分法.
主要方法:
- 在2023年从医学杂志 (巴尔的摩) 的1976篇文章的图书统计分析.
- 应用Kano模型将作者分为五类:追随者,领导者,合作伙伴,贡献者和合作者.
- 分析作者位置,共同作者网络和多作者率 (MAR),可视化使用网络地图和卡诺图.
主要成果:
- 确定了一个复杂的作者角色网络,超越了简单的领导/支持区别.
- 证实了这一假设,中国展示了领导力 (1148篇出版物) 和美国作为合作者 (51篇出版物,19.61% MAR).
- 卡诺模型有效地将作者分为五个不同的角色,通过地理和网络地图可视化.
结论:
- 卡诺模型为了解医学研究中不同作者贡献提供了一个有效的工具.
- 认识到作者角色的复杂生态系统对于推进医学知识至关重要.
- 这种方法可以促进跨科学学科贡献的更多协作和包容性认可.
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