使用随机效应模型分析未来医学学生的多个小访谈 - - 从理论到实践
Chezko Malachi Peligrino Castro1, Nicola Phillips1, Karen Grant1
1Medical School, Lancaster University Medical School, Lancaster, UK.
Journal of medical education and curricular development
|February 2, 2026
概括
多次小面试 (MMI) 分析显示,申请人的能力是最大的差异来源. 一个新的R Shiny应用程序标准化了MMI分数,并提供了面试官反,改善了医学院招生.
科学领域:
- 医学教育 医学教育
- 心理测量 心理测量 心理测量
- 统计建模 统计建模
背景情况:
- 多次小面试 (MMI) 是医学院招生的标准.
- 在MMI评分中的不一致性可能源于面试官的变化和站点难度.
研究的目的:
- 使用累积探针混合模型分析多次微型采访 (MMI) 数据.
- 开发一个用户友好的R Shiny应用程序,用于标准化MMI分数,并向非统计专家提供反.
主要方法:
- 使用累积探针混合模型来分析MMI数据,并考虑隐藏的变化源.
- 开发了一个R Shiny应用程序,使统计方法可用于标准化得分和生成反.
主要成果:
- 申请人的能力占MMI分数差异的22.94%.
- 访谈者的变化对差异贡献了10.79%,而站点难度对差异的影响很小 (2.23%).
- 站间可靠性是可以接受的 (克朗巴赫的α = 0.7072).
结论:
- 开发的统计方法和应用程序为MMI评分分析提供了强大的方法.
- 该工具有助于向面试人员提供反,并识别有效的评估站.
- 该方法可被其他医疗机构适应,以更公平地选择候选人.
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