在孕产妇健康研究中进行主题分析的生成人工智能:使用大型语言模型编码半结构面试
Shan Qiao1,2, Xingyu Fang3,4,5, Junbo Wang6
1Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, USA.
Applied psychology. Health and well-being
|May 16, 2025
概括
生成型人工智能 (GenAI) 可以高效地编码定性面试数据,将编码时间减少81%,准确度超过80%. 需要进一步的研究来解决这种自动化主题分析方法的局限性.
科学领域:
- 定性研究方法 定性研究方法
- 计算语言学 计算语言学
- 医疗保健中的人工智能
背景情况:
- 定性数据的主题分析依赖于编码半结构面试成绩单.
- 手动编码是劳动密集型和耗时的,阻碍了有效的数据分析.
- 生成型人工智能 (GenAI) 为提高定性编码效率提供了潜在的解决方案.
研究的目的:
- 提出和评估一个使用GenAI的计算管道,从采访成绩单中自动提取主题.
- 与手动方法相比,评估GenAI在感应编码中的效率和准确性.
主要方法:
- 利用ChatGPT对产妇护理提供者的面试成绩单进行诱导编码.
- 开发了结构化提示,以指导ChatGPT在没有预定义方案的情况下生成和总结代码.
- 通过比较人工智能生成的代码与手工衍生代码来评估GenAI的性能.
主要成果:
- 在检测和总结定性访谈数据中的代码方面,GenAI表现出显著的前景.
- 在诱导编码任务中,ChatGPT的准确度超过了80%.
- 基因人工智能将编码所需的时间减少了令人印象深刻的81%.
结论:
- 基因人工智能模型可以有效地处理语言数据,用于定性研究中的语义识别.
- 包括潜在的不准确性,偏见和隐私问题在内的挑战需要仔细考虑.
- 未来的研究应该专注于改进GenAI模型以提高可靠性和解决定性应用的局限性.
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