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一个积极的转折:大型语言模型可以帮助导演通过患者自己的话来评估项目

Leah Russell Flaherty¹, Kendra H Oliver^2,3

¹Allegheny Health Network, Psychiatry and Behavioral Health Instiute, Pittsburgh, PA, USA.

|December 27, 2024

概括

大型语言模型 (LLM) 有效地分析患者的定性反以评估程序. 这种方法捕获了丰富的,以患者为中心的洞察力,超越了定量数据,增强了疼痛心理学研究.

科学领域:

疼痛心理学疼痛心理学
项目评估项目评估
定性数据分析数据分析.

背景情况:

定性反对于计划评估至关重要,为患者的体验提供了洞察力,通常仅通过定量数据就无法获得.
仅依靠量化指标就有可能忽视患者报告的结果和生活经验的细微差别.

研究的目的:

评估使用大型语言模型 (LLM) 分析疼痛教育计划中参与者的定性反的可行性.
探索LLM辅助分析在发现以患者为中心的课程改进主题方面的实用性.

主要方法:

采用了双方法方法,结合了LLM辅助分析 (ChatGPT) 和手动主题审查.
分析了82名参与者基于Empowered Relief技能的疼痛教育课程的定性反应.

主要成果:

使用ChatGPT进行的LLM分析确定了7个主要主题:特定音频文件的使用,思维方式,技术,社区和空间,知识,工具和方法以及自我意识.
主题分析直接从患者自己的话语中产生了丰富的,意想不到的见解,指导了计划评估.

结论:

从LLM衍生分析提供了一种高效和人体工程学方法,用于提取有价值的定性见解,补充传统的评估方法.
这种方法使计划主管能够更广泛地评估治疗结果,并纳入超越残疾措施的患者观点.

关键词:

生成型的人工智能 (GenAI)大型语言模型 (LLM)疼痛心理学疼痛心理学定性分析是一种定性分析.专题分析专题分析

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