在对医学学生绩效评估进行排名时,比较机器学习模型和人类评分器.
Jonathan Kibble1, Jeffrey Plochocki2
1Both authors are with University of Central Florida College of Medicine. is Professor of Medical Education; and.
Journal of graduate medical education
|August 28, 2023
概括
一个新的标题可靠地排名了医学学生绩效评估 (MSPEs). 机器学习模型检测到积极的情绪,但无法可靠地对居留计划评估中MSPEs进行排名.
科学领域:
- 医疗教育 医学教育
- 自然语言处理自然语言处理.
- 住院招生 住院招生
背景情况:
- 医学学生绩效评估 (MSPE) 是长篇叙事总结.
- 评估众多的中小企业对居住计划提出了挑战.
研究的目的:
- 开发一个标题来评估MSPE叙述.
- 将机器学习模型 (MLM) 进行比较,以根据积极性对MSPEs进行排名.
主要方法:
- 30个被取消识别的小微企业被手动评分,使用新的标题.
- 该学院的教师根据ACGME能力评估了中小企业.
- 3家商业MLM公司对小微企业进行了全球情绪分析.
- 相关性分析比较了教师和MLM排名.
主要成果:
- 该标题的教师间评分器可靠性很高 (ICC=0.864).
- 院系排名显示出强烈的相关性 (r=0.758).
- 多国经营企业在所有中小企业中检测到积极的情绪,但在排名中没有显著的相关性,无论是彼此之间还是与教师之间.
- 该标题是可行的,并增加了最小的时间.
结论:
- 该标题提供了可靠的MSPEs评分和排名.
- 多媒体市场准确地识别了积极的情绪,但缺乏对小微企业的排名可靠性.
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