在反思性主题分析中反思LLM支持:一项探索性研究
Magnhild Vikan1, Ramtin Aryan2, Mari Serine Kannelønning3
1Department of Nursing and Health Promotion, Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway.
Qualitative health research
|September 8, 2025
概括
基础大语言模型 (LLM) 目前为反射主题分析 (RTA) 提供有限的支持,并且没有提高效率. 人类研究人员对于高质量的定性分析和保持认识论完整性至关重要.
科学领域:
- 定性研究方法 定性研究方法
- 人工智能在健康科学中的应用
- 文本分析 文本分析
背景情况:
- 像ChatGPT这样的大型语言模型 (LLM) 的出现激起了关于它们在定性研究中的有用性的辩论.
- 反射主题分析 (RTA) 是一种关键的定性方法,强调研究者的主观性,以深入理解.
- 人们对LLM的潜力持乐观态度,但质量分析中对其有意义的解释能力受到质疑.
研究的目的:
- 调查LLM如何支持健康科学访谈数据的反射主题分析 (RTA).
- 确定有效的及时策略,在RTA中利用LLMs.
- 突出潜在的挑战,并促进讨论LLMs在定性研究中的作用.
主要方法:
- 使用离线大型语言模型 (LLM) 来分析来自健康科学研究的采访文本.
- 进行了一系列测试,反映了RTA的阶段.
- 基于测试见解的提示的代改进导致了推的提示策略.
主要成果:
- 基础LLM目前仅为RTA提供有限的支持,并且不会提高分析效率.
- 法律法学可能有助于识别研究人员观点的差距.
- 有效利用需要明确的策略,以避免误导性提示和错误等问题.
结论:
- 高质量的RTA需要人类参与,全面的熟悉和方法专业知识.
- 基础的LLM,在目前的状态下,不能取代人类研究人员提供的细微的解释和认识论完整性.
- 需要进一步的研究和战略实施,以探索LLMs在定性研究中的潜力.
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