一项混合方法研究,比较医学教育研究中的人为主导和ChatGPT驱动的定性分析
Takeshi Kondo1,2, Junichiro Miyachi2,3, Anders Jönsson4
1Department of General Medicine/Family & Community Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan.
Nagoya journal of medical science
|January 9, 2025
概括
这项研究探讨了使用ChatGPT进行医学教育研究的主题分析. 虽然ChatGPT看起来有前途,但人类监督对于深入,丰富上下文的定性分析至关重要.
科学领域:
- 医疗教育 医学教育
- 研究中的人工智能.
背景情况:
- 定性研究对于理解医学教育至关重要,但需要花费大量时间.
- 技术,就像大型语言模型一样,为简化定性数据分析提供了潜力.
研究的目的:
- 评估ChatGPT (GPT-4) 在进行医学定性研究的主题分析中的适用性.
- 将ChatGPT的感应主题分析能力与人类定性分析进行比较.
主要方法:
- 采用了融合设计混合方法研究.
- 聊天GPT (GPT-4) 使用主题分析方法分析了发表的医学研究文章中的采访数据.
- 三位评估者将ChatGPT的分析与人类进行的分析进行了比较.
主要成果:
- 聊天GPT在数据提取和总结方面表现出强项,经常与人类分析取得表面的相似性.
- 然而,ChatGPT表现出可变的可转移性和混合的深度得分,确定了包括即时污染和缺乏理论导出在内的主题.
- 评估人员注意到ChatGPT的趋势是"厚厚的描述"缺陷和基于上下文的限制.
结论:
- 聊天GPT可以协助定性数据分析,但需要仔细的快速工程和人类审查,以减轻诸如快速污染等问题.
- 人类投入对于提供研究背景,理论深度和确保医学定性研究中全面分析至关重要.
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