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在NBME学科考试中学生表现的多变量建模
Seth M Alexander1,2, Christina L Shenvi3, Kimberley R Nichols4
1Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, USA.
统计模型可以预测医学学生在临床考试中的表现,使用国家医学检查委员会 (NBME) 的自我评估. 预测值因学科而异,为学生的成功提供了适度的见解.
科学领域:
- 医学教育 医学教育
- 生物统计学 生物统计学
- 健康 专业 教育 卫生 专业 教育
背景情况:
- 目前,预测医学学生在临床科目考试中的表现的工具是有限的.
- 国家法医审查委员会 (NBME) 的自我评估为预测建模提供了潜在的数据来源.
研究的目的:
- 开发和评估统计模型,将NBME自我评估表现与学生在临床学科考试中的成功联系起来.
- 评估这些模型在不同医学学科的预测价值.
主要方法:
- 使用医学学生的NBME自我评估数据创建了多变量回归模型.
- 模型控制了包括自我评估时间,USMLE第一步分数和学术季度在内的因素.
- 学生在六项临床科目考试 (医学,外科,家庭医学,产科医生,儿科医生,精神病学) 的表现是结果变量.
主要成果:
- 符合线性回归要求,在所有受试者中具有统计学意义的模型 (p<0.001).
- 模型的预测功率 (R平方) 从0.1799到0.4915不等,表明相关性较弱至中等.
- 预测的强度因具体的临床受试者和绩效指标 (百分比正确或百分位数) 而有很大差异.
结论:
- 使用NBME自我评估的统计模型表明,学生在临床学科考试中的表现具有较弱到中等的预测价值.
- 模型的准确性在很大程度上取决于正在评估的特定医学学科.
- 未来的研究应该专注于改进模型的额外变量,并评估它们对降低学生失败率的影响.
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