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整合课程中的形成性评估:使用美国国家医学考试委员会(NBME)定制考试题目识别在USMLE第一步考试中表现不佳的风险学生。

Formative Assessment in an Integrated Curriculum: Identifying At-Risk Students for Poor Performance on USMLE Step 1 Using NBME Custom Exam Questions.

作者信息

Brenner Judith M, Bird Jeffrey B, Willey Joanne M

机构信息

J.M. Brenner is associate dean of curricular integration and assessment, Hofstra Northwell School of Medicine, Hempstead, New York. J.B. Bird is assessment and evaluation analyst, Hofstra Northwell School of Medicine, Hempstead, New York. J.M. Willey is professor and chair, Department of Science Education, Hofstra Northwell School of Medicine, Hempstead, New York.

出版信息

Acad Med. 2017 Nov;92(11S Association of American Medical Colleges Learn Serve Lead: Proceedings of the 56th Annual Research in Medical Education Sessions):S21-S25. doi: 10.1097/ACM.0000000000001914.

Abstract

PURPOSE

The Hofstra Northwell School of Medicine (HNSOM) uses an essay-based assessment system. Recognizing the emphasis graduate medical education places on the United States Medical Licensing Examination (USMLE) Step exams, the authors developed a method to predict students at risk for lower performance on USMLE Step 1.

METHOD

Beginning with the inaugural class (2015), HNSOM administered National Board of Medical Examiners (NBME) Customized Assessment Service (CAS) examinations as formative assessment at the end of each integrated course in the first two years of medical school. Using preadmission data, the first two courses in the educational program, and NBME score deviation from the national test takers' mean, a statistical model was built to predict students who scored below the Step 1 national mean.

RESULTS

A regression equation using the highest Medical College Admission Test (MCAT) score and NBME score deviation predicted student Step 1 scores. The MCAT alone accounted for 21% of the variance. Adding the NBME score deviation from the first and second courses increased the variance to 40% and 50%, respectively. Adding NBME exams from later courses increased the variance to 52% and 64% by the end of years one and two, respectively. Cross-validation demonstrated the model successfully predicted 63% of at-risk students by the end of the fifth month of medical school.

CONCLUSIONS

The model identified students at risk for lower performance on Step 1 using the NBME CAS. This model is applicable to schools reforming their curriculum delivery and assessment programs toward an integrated model.

摘要

目的

霍夫斯特拉北威医学院(HNSOM)采用基于论文的评估系统。鉴于研究生医学教育对美国医师执照考试(USMLE)各阶段考试的重视,作者开发了一种方法来预测USMLE第一步考试成绩较低风险的学生。

方法

从首届班级(2015年)开始,HNSOM在医学院前两年的每门综合课程结束时,将美国国家医学考试委员会(NBME)定制评估服务(CAS)考试作为形成性评估进行管理。利用入学前数据、教育计划中的前两门课程以及与全国考生平均分的NBME分数偏差,建立了一个统计模型来预测得分低于第一步全国平均分的学生。

结果

使用最高的医学院入学考试(MCAT)分数和NBME分数偏差的回归方程预测学生的第一步考试成绩。仅MCAT就占方差的21%。加上第一和第二课程的NBME分数偏差,方差分别增加到40%和50%。到第一年末和第二年末,加上后续课程的NBME考试,方差分别增加到52%和64%。交叉验证表明,到医学院第五个月末,该模型成功预测了63%的有风险学生。

结论

该模型使用NBME CAS识别出第一步考试成绩较低风险的学生。该模型适用于将课程交付和评估计划改革为综合模式的学校。

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