使用大型语言模型支持戒烟尝试的可接受性:在吸烟的非洲裔美国人中进行的一项定性研究
Warren McKinney1, Devyn Fernholz1, Serguei Pakhomov2
1Hennepin Healthcare Research Institute, Minneapolis, MN.
概括
非洲裔美国人愿意使用大型语言模型 (LLM) 应用程序戒烟,但对人工智能技术表示担忧. 将LLM干预措施与社区投入量身定制对于信任和有效性至关重要.
科学领域:
- 数字健康数字健康
- 健康差异 在健康上的差异
- 医疗保健中的人工智能
背景情况:
- 非洲裔美国人在与烟草有关的疾病和戒烟资源方面经历了显著的差异.
- 数字健康干预措施,特别是那些使用大型语言模型 (LLM) 的干预措施,显示出改善参与和戒烟尝试的潜力.
- 在非洲裔美国人中,缺乏对基于LLM的干预措施可接受性的理解,这凸显了对量身定制方法的需求.
研究的目的:
- 评估非洲裔美国人对LLM基础干预措施的看法.
- 为开发一个基于LLM的聊天机器人提供信息,旨在支持这个社区的戒烟尝试.
主要方法:
- 远程聚焦小组与明尼苏达州的非洲裔美国成年吸烟者进行.
- 讨论探讨了基于LLM的健康干预措施和聊天机器人的可接受性.
- 焦点组的成绩单由两位分析师独立编码,使用共享的编码簿.
主要成果:
- 21名成年人参与,其中57.1%的人认为自己是女性,平均年龄为45.6岁.
- 关键主题包括对LLM/AI的熟悉程度有限,愿意使用LLM应用程序戒烟,以及影响聊天机器人可接受性的特定特征感知.
- 参与者表示需要用户友好和值得信赖的AI健康工具.
结论:
- 非洲裔美国人对健康干预的LLM持有微妙的观点,认识到知识差距和担忧的潜在好处.
- 建立信任和可接受性需要优先考虑社区参与和伦理考虑在LLM干预开发.
- 由LLM驱动的健康解决方案必须在各种社区中具有包容性和有效性.
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