探索自然语言处理的使用,以了解实习生和教师对可信任的专业活动评估的情绪
Devin Johnson1, Sonaina Chopra2, Elif Bilgic3
1is a recent graduate of the PhD program, Department of Psychology, Neuroscience & Behaviour, McMaster University, Hamilton, Ontario, Canada.
Journal of graduate medical education
|June 17, 2024
概括
对医学教育调查的自然语言处理 (NLP) 分析揭示了居民和教师之间以及医疗专业之间对可信任专业活动 (EPA) 评估的情绪差异.
科学领域:
- 医学教育 医学教育
- 自然语言处理自然语言处理.
- 情绪分析 情绪分析
背景情况:
- 人工智能 (AI) 和自然语言处理 (NLP) 在医学教育中越来越多地用于分析文本数据.
- 在医学培训中理解情绪对于改善教育经验和成果至关重要.
研究的目的:
- 应用NLP技术来分析居民和教师对可信任专业活动 (EPA) 评估表达的情绪.
- 根据专业角色和医学专业,识别与EPA评估相关的情绪反应的变化.
主要方法:
- 从对EPA评估的情绪调查中对开放式文本响应的分析.
- 利用NLP的验证情感词典来量化响应中的积极词汇频率.
- 检查了教师与住院人员之间的情绪情绪变化,以及儿科,整体外科和急诊医学专业的情绪变化.
主要成果:
- 从教师和住院人员的66条短信回复中分析了情绪.
- 医学专业之间发现了积极词数的显著差异.
- 与紧急医疗相比,一般外科住院人员和教师对EPA评估的积极情绪较少.
结论:
- 对文本数据的NLP分析有效地捕捉和区分与EPA评估相关的情绪.
- 在居民和教师之间确定了与EPA评估有关的独特的情感景观.
- 在对EPA评估的情绪反应中显示出显著的跨专业差异.
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