基于自然频率树与条件概率公式的培训,用于医学学生对查测试预测值的估计:一种随机对照试验
Soela Kim1, Soyun Kim1, Yong-Jun Choi2,3
1Institute of Health Policy and Management, Seoul National University Medical Research Center, Seoul, South Korea.
BMC medical education
|October 25, 2024
概括
基于自然频率树的培训 (NF-TT) 并没有超过医学学生的传统配方培训. 然而,NF-TT对于没有先前培训的学生来说,在估计医疗测试预测值方面更有效.
科学领域:
- 医学教育 医学教育
- 生物统计学 生物统计学
- 临床决策 临床决策
背景情况:
- 医学学生和专业人士经常误解医学测试结果,导致不理想的决策.
- 基于自然频率树的训练 (NF-TT) 是一种提议的方法,以提高对预测测试值的理解.
- 这项研究将NF-TT与传统的基于条件概率公式 (CP-FT) 的训练进行了比较.
研究的目的:
- 为了比较NF-TT与CP-FT在医学学生估计医疗测试预测值的能力方面的有效性.
- 确定影响NF-TT有效性的学生特征.
- 评估学习技能转移到非医疗环境中的情况.
主要方法:
- 一项随机对照试验在韩国对231名医学学生进行.
- 参与者被分配观看15分钟的NF-TT或CP-FT视频.
- 主要结局是估计预测值的准确性,在干预后立即测量,并在一个月后进行随访.
主要成果:
- 总的来说,NF-TT在培训后或随访后立即估计预测值方面没有比CP-FT显著的优势.
- 在没有先前相关培训的参与者中,NF-TT在随访时明显比CP-FT更有效.
- 在这个子组中,NF-TT还证明了学习向非医疗环境的优越转移.
结论:
- 在医学教育的早期阶段引入NF-TT可能是最有益的,在广泛接触基于配方的方法之前.
- 针对性地实施NF-TT可以提高特定学生群体的概率推理技能.
- 进一步的研究可能会探索NF-TT的最佳课程整合.
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