The Allure of "Objective" Data in US Medical Student Assessment and Residency Selection-Are Faulty Data Better than None?

IF 2.4 3区 教育学 Q2 EDUCATION, SCIENTIFIC DISCIPLINES
Debra Klamen, Richard Austin
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引用次数: 0

Abstract

Recent commentaries have urged reconsideration of the shift away from tiered grading in US medical schools and the removal of numerical scores on the US Medical Licensing Exam Step 1 examination. They suggest the move to pass/fail may negatively impact decision-making, especially affecting residency selection. By arguing for the need for data to help residency programs stratify applicants, they risk overstating the objectivity of "hard data" and overlooking the long-term goal of educating future doctors. We examine the history of tiered grading and explore the evidence that led to shifts away from such grading formats. Tiered systems often rely on multiple-choice question assessments that introduce bias and assess only medical knowledge. They show extreme variability and grade inflation, and studies have consistently demonstrated racial and gender disparities. Moreover, Step 1, which was never designed to stratify students' performances, has not been shown to predict future physician clinical success in any significant way. Claims that the shift away from tiered grading has resulted in detrimental student behaviors or bias against students from specific schools are unsubstantiated. In contrast, pass/fail grading leads to improved student well-being, intrinsic motivation, and mastery-oriented learning environments. Returning to competitive assessment, which stratifies residency applications, prioritizes a comfortable system over one with substantial validity evidence. Our efforts are better spent on developing new programmatic systems that incorporate holistic data, leverage artificial intelligence, and combine narrative feedback to assess our learners and prepare them to be competent physicians. We should strive to link assessments to long-term outcomes before using them to determine students' fates. We should not return to familiar data because they help residency programs stratify learners at the expense of their learning.

“客观”数据在美国医学生评估和住院医师选择中的吸引力——有缺陷的数据比没有好吗?
最近的评论敦促重新考虑美国医学院取消分层评分和取消美国医学执照考试第一步考试的数字分数。他们认为,通过/不通过的做法可能会对决策产生负面影响,尤其是对住院医师的选择。他们辩称需要数据来帮助住院医师项目对申请人进行分层,这可能会夸大“硬数据”的客观性,而忽视了培养未来医生的长期目标。我们检查分级分级的历史,并探讨导致从这种分级格式转移的证据。分级系统通常依赖于引入偏见的多项选择题评估,并且只评估医学知识。他们表现出极端的差异和分数膨胀,研究一直表明种族和性别差异。此外,步骤1从来没有被设计用来对学生的表现进行分层,也没有被证明能以任何重要的方式预测未来医生的临床成功。有人声称,分级评分制度的改变导致了有害的学生行为或对特定学校学生的偏见,这是没有根据的。相比之下,及格/不及格评分会改善学生的幸福感、内在动机和以掌握为导向的学习环境。回到竞争评估,这将住院申请分层,优先考虑一个舒适的系统,而不是一个有大量有效证据的系统。我们的努力最好花在开发新的程序化系统上,这些系统可以整合整体数据,利用人工智能,并结合叙事反馈来评估我们的学习者,使他们成为合格的医生。在用评估来决定学生的命运之前,我们应该努力将评估与长期结果联系起来。我们不应该回到熟悉的数据,因为它们帮助住院医师计划以牺牲学习者的学习为代价对学习者进行分层。
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来源期刊
Teaching and Learning in Medicine
Teaching and Learning in Medicine 医学-卫生保健
CiteScore
5.20
自引率
12.00%
发文量
64
审稿时长
6-12 weeks
期刊介绍: Teaching and Learning in Medicine ( TLM) is an international, forum for scholarship on teaching and learning in the health professions. Its international scope reflects the common challenge faced by all medical educators: fostering the development of capable, well-rounded, and continuous learners prepared to practice in a complex, high-stakes, and ever-changing clinical environment. TLM''s contributors and readership comprise behavioral scientists and health care practitioners, signaling the value of integrating diverse perspectives into a comprehensive understanding of learning and performance. The journal seeks to provide the theoretical foundations and practical analysis needed for effective educational decision making in such areas as admissions, instructional design and delivery, performance assessment, remediation, technology-assisted instruction, diversity management, and faculty development, among others. TLM''s scope includes all levels of medical education, from premedical to postgraduate and continuing medical education, with articles published in the following categories:
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