在母亲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 (LLM) 编码半结构面试.
Shan Qiao1, Xingyu Fang2,3,4, Junbo Wang5
1Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina.
概括
生成型人工智能 (GenAI) 显著加快了面试成绩单的定性编码,将编码时间减少了81%,准确度超过80%. 这项技术为专题分析提供了一种有希望的,高效的方法.
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
- 医疗保健服务研究 医疗服务研究
背景情况:
- 对定性数据的专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.
- 评估GenAI在没有预定义方案的情况下执行感应编码的效率和准确性.
主要方法:
- 利用ChatGPT对来自南卡罗来纳州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在生成和总结代码.
- 将人工智能生成的代码与手动衍生代码进行比较,以评估性能.
主要成果:
- 基因人工智能在检测和总结采访成绩单中的代码方面表现有前途.
- 在诱导编码任务中,ChatGPT的准确度超过了80%.
- GenAI将编码所需的时间减少了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.
- 包括不准确性,偏见和隐私在内的挑战需要仔细考虑和缓解.
- 需要进一步的研究来完善GenAI模型,以提高定性数据分析的可靠性.
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