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根据先前的学业指标预测美国医师执照考试第二步临床知识分数。

Predicting United States Medical Licensure Examination Step 2 clinical knowledge scores from previous academic indicators.

作者信息

Monteiro Kristina A, George Paul, Dollase Richard, Dumenco Luba

机构信息

Office of Medical Education, The Warren Alpert Medical School of Brown University, Providence, RI, USA.

出版信息

Adv Med Educ Pract. 2017 Jun 19;8:385-391. doi: 10.2147/AMEP.S138557. eCollection 2017.

DOI:10.2147/AMEP.S138557
PMID:28670150
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5482402/
Abstract

The use of multiple academic indicators to identify students at risk of experiencing difficulty completing licensure requirements provides an opportunity to increase support services prior to high-stakes licensure examinations, including the United States Medical Licensure Examination (USMLE) Step 2 clinical knowledge (CK). Step 2 CK is becoming increasingly important in decision-making by residency directors because of increasing undergraduate medical enrollment and limited available residency vacancies. We created and validated a regression equation to predict students' Step 2 CK scores from previous academic indicators to identify students at risk, with sufficient time to intervene with additional support services as necessary. Data from three cohorts of students (N=218) with preclinical mean course exam score, National Board of Medical Examination subject examinations, and USMLE Step 1 and Step 2 CK between 2011 and 2013 were used in analyses. The authors created models capable of predicting Step 2 CK scores from academic indicators to identify at-risk students. In model 1, preclinical mean course exam score and Step 1 score accounted for 56% of the variance in Step 2 CK score. The second series of models included mean preclinical course exam score, Step 1 score, and scores on three NBME subject exams, and accounted for 67%-69% of the variance in Step 2 CK score. The authors validated the findings on the most recent cohort of graduating students (N=89) and predicted Step 2 CK score within a mean of four points (SD=8). The authors suggest using the first model as a needs assessment to gauge the level of future support required after completion of preclinical course requirements, and rescreening after three of six clerkships to identify students who might benefit from additional support before taking USMLE Step 2 CK.

摘要

使用多个学术指标来识别有难以完成执照要求风险的学生,为在高风险执照考试(包括美国医师执照考试(USMLE)第二步临床知识(CK))之前增加支持服务提供了机会。由于本科医学招生人数增加和住院医师空缺有限,第二步CK在住院医师项目主任的决策中变得越来越重要。我们创建并验证了一个回归方程,根据之前的学术指标预测学生的第二步CK分数,以识别有风险的学生,并有足够的时间在必要时提供额外的支持服务。分析使用了2011年至2013年间三个学生队列(N = 218)的数据,这些学生有临床前平均课程考试成绩、美国国家医学考试委员会科目考试成绩以及USMLE第一步和第二步CK成绩。作者创建了能够根据学术指标预测第二步CK分数以识别有风险学生的模型。在模型1中,临床前平均课程考试成绩和第一步成绩占第二步CK分数方差的56%。第二系列模型包括临床前平均课程考试成绩、第一步成绩以及三项NBME科目考试的成绩,占第二步CK分数方差的67%-69%。作者在最近一批毕业学生(N = 89)中验证了研究结果,并预测第二步CK分数的平均误差在4分以内(标准差 = 8)。作者建议使用第一个模型作为需求评估,以衡量完成临床前课程要求后所需的未来支持水平,并在六个实习轮转中的三个轮转后重新筛选,以识别在参加USMLE第二步CK之前可能从额外支持中受益的学生。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a196/5482402/b325c16ec6e2/amep-8-385Fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a196/5482402/b325c16ec6e2/amep-8-385Fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a196/5482402/b325c16ec6e2/amep-8-385Fig1.jpg

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