在招聘中检测对话主题 非洲裔美国人参与者致电我们所有人的机器学习研究计划:模型开发和验证研究
Priscilla Pemu1,2, Michael Prude2, Atuarra McCaslin2
1Department of Medicine, Morehouse School of Medicine, Atlanta, GA, United States.
JMIR formative research
|July 17, 2025
概括
了解我们所有人研究计划 (AoURP) 中的招聘对话可以改善临床试验的多样性. 影响入学的关键话题包括后续预约,精准医学解释和解决参与者的担忧.
科学领域:
- 生物医学研究的研究.
- 在健康方面存在差异.
- 临床试验招聘工作人员
背景情况:
- 临床研究的多样性对于公平的健康结果至关重要.
- 代表性不足的群体,特别是非裔美国人,在研究中面临着历史的信任障碍.
- 我们所有人的研究计划 (AoURP) 旨在增加生物医学研究的多样性.
研究的目的:
- 分析 AoURP 中的招聘对话,以确定招聘影响者.
- 改善临床研究中代表性不足的人群的参与和招聘策略.
主要方法:
- 使用机器学习进行内容分析的观察性,回顾性研究.
- 结构主题建模在招聘电话中确定了对话主题.
- 分析的重点是莫尔豪斯医学院RAS的电话 (2021年2月至2022年4月).
主要成果:
- 从45个估计的主题中确定了12个连贯的主题.
- 成功招生与讨论后续预约,COVID-19安全性,精准医学以及解决问题 (成本,保险,健康担忧) 相关.
- 非注册对话经常涉及技术问题和物理测量的描述.
结论:
- 机器学习有效地识别了招聘对话中的主题,并尽量减少主观性.
- 这种方法为改善服务不足社区的临床试验招聘提供了洞察力.
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