使用自然语言处理来评估监督员质量 基于能力的医学教育中的叙事评论
概括
一个新的自然语言处理 (NLP) 模型准确地评分医疗教育中的叙事评估评论,节省时间并改善居民发展的反质量. 这种工具增强了对监管机构评论的评估.
科学领域:
- 医学教育 医学教育
- 自然语言处理自然语言处理.
- 评估 质量 评估 评价
背景情况:
- 叙事评估评论对于学习者发展和医学教育中的推广至关重要.
- 评估这些评论的质量是具有挑战性和耗时的.
- 现有的工具,如学习质量评估 (Quality of Assessment for Learning,QuAL) 工具存在,但需要大量的手工努力.
研究的目的:
- 开发和验证一种自然语言处理 (NLP) 模型,以使用QuAL工具自动评分叙事主管评论.
- 评估模型预测整体QuAL分数和特定子分数的能力.
主要方法:
- 从紧急医疗住院计划中提取并删除了2500个可信任的专业活动 (EPA) 评估.
- 有50名教职员工和居民使用QuAL分数对评论进行评价.
- 开发并测试了一种NLP模型,以根据这些人类评级预测QuAL分数.
主要成果:
- NLP模型取得了卓越的表现,在87%的案例中预测了人为评级的QuAL分数在1分之内.
- 与改进建议相关的子任务和将绩效与建议联系起来显示出高度平衡的准确性 (分别为85%和82%).
- 有意义的改进建议被确定为高质量的反的关键差异化因素.
结论:
- 开发的NLP模型可以显著减少评估医疗教育中监督者评论质量的时间.
- 该工具为大量评估评论提供了自动评分的潜力.
- 该模型可用于实时教师反,以及在各个机构层面量化/跟踪评估质量.
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