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通过人工智能进行的定性分析与传统方法的比较
Marcela Salazar1, May Chaw1, Yvette Hellier1
1University of California San Francisco School of Pharmacy, Department of Clinical Pharmacy, San Francisco, CA, USA.
American journal of pharmaceutical education
|October 16, 2025
概括
像GPT-4这样的生成人工智能,可以帮助人类研究人员进行健康专业教育的定性分析. 然而,由于检测细微差别和低频代码的局限性,它无法取代人类监督.
科学领域:
- 卫生专业教育研究 卫生专业教育研究
- 定性分析方法 定性分析方法.
- 在研究中的人工智能.
背景情况:
- 由于培训缺口和耗时分析,质量研究在卫生专业教育中未得到充分利用.
- 生成型人工智能 (AI),特别是像GPT-4这样的大型语言模型,为简化定性研究提供了机会.
- 在这个领域,人工智能生成的代码和主题的准确性需要进一步调查.
研究的目的:
- 在卫生专业教育中,与传统人体分析相比,以GPT-4为辅助的定性分析的特征.
- 评估GPT-4生成的代码书和主题的准确性,对齐性,相关性和适当性.
主要方法:
- 在最初的人类分析后,使用GPT-4重新分析了两个卫生专业数据集.
- 将GPT-4的输出 (代码书和主题) 与之前的人类发现进行了比较.
- 用不同的评级和协作讨论来评估GPT-4的优缺点.
主要成果:
- 对36份调查答复和7份采访成绩单的GPT-4分析显示,这些概念与人类识别的概念普遍一致.
- GPT-4努力检测低频代码并建立连贯的代码关系.
- GPT-4在主题描述和引用选择中的细微差别是有限的.
结论:
- GPT-4可以作为人类领导的定性分析的辅助工具,但不能取代它.
- 研究人员需要对定性研究和数据集有充分的了解,才能有效地利用人工智能工具并识别局限性.
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