人工智能和成研究中的耻辱:来自HEALing社区的见解 研究联盟会议
Nabila El-Bassel1, James L David, Eric Aragundi
1Columbia University School of Social Work (NEB, JLD, EW, LG, TH, VF, DAGE, SNB); Department of Statistics Columbia University (EA, TZ); School of Public Health at the University of North Texas Health Science Center (STW); National Institute on Drug Abuse (RC); Department of Psychiatry, Columbia University Irving Medical Center, New York State Psychiatric Institute (ANCC); Columbia University Information Technology (MC, PD, MA); Albert Einstein College of Medicine (DL); City University of New York School of Public Health (NS, TH); Department of Public Health Sciences, Biostatistics, University of Miami (DF).
人工智能 (AI) 分析了社区会议记录,以了解在选择基于证据的实践 (EBP) 时关于耻辱的讨论. 较高的少数民族人口与更频繁的耻辱讨论相关,特别是关于种族差异的讨论.
科学领域:
- 公共卫生 公共卫生
- 医疗保健服务研究 医疗服务研究
- 医疗保健中的人工智能
背景情况:
- 社区联盟在实施基于证据的实践 (EBP) 以解决公共卫生问题方面发挥着至关重要的作用.
- 了解围绕耻辱的讨论对于有效实施EBP至关重要,特别是在不同的社区.
- 治愈社区研究旨在通过社区层面的干预来减少过量死亡.
研究的目的:
- 利用人工智能分析社区联盟会议记录,评估与EBP相关的耻辱讨论.
- 检查跨联盟的耻辱讨论的变化及其与种族,种族和种族不平等的关联.
- 确定少数群体的比例是否会影响耻辱讨论的频率.
主要方法:
- 使用自然语言处理 (NLP),机器学习 (ML) 和大型语言模型 (LLM).
- 雇佣ChatGPT企业进行数据编码,确保遵守通用数据保护条例 (GDPR) 和HIPAA.
- 在HEALing社区研究中分析了社区联盟的会议记录.
主要成果:
- 在选择EBP时,联盟之间的耻辱讨论频率存在显著差异.
- 在治疗阿片类药物使用障碍的药物背景下,人们更经常讨论耻辱感.
- 一个县的种族/民族少数群体人口比例增加与EBP讨论和耻辱之间更强的联系有关,当提到种族/民族差异时,耻辱讨论的可能性增加了57%.
结论:
- 人工智能与人类的协作可以通过分析诸如会议记录之类的定性数据来增强社区参与的研究.
- 人工智能工具,如LLM,提供实时功能,以改善基于社区的研究过程.
- 调查结果强调了解决EBP选择中的耻辱的重要性,特别是在多元化和服务不足的社区.
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