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呼吁在以能力为基础的医学教育中采用混合方法:我们如何防止课程和评估的过度拟合。

A Call for Mixed Methods in Competency-Based Medical Education: How We Can Prevent the Overfitting of Curriculum and Assessment.

机构信息

N.S. Hoang is research associate and specialty investigator in simulation and curriculum development, Stanford School of Medicine, Stanford, California. J.N. Lau is clinical professor, Department of General Surgery, and director, Goodman Surgical Education Center, Stanford School of Medicine, Stanford, California.

出版信息

Acad Med. 2018 Jul;93(7):996-1001. doi: 10.1097/ACM.0000000000002205.

Abstract

Competency-based medical education (CBME) has been the subject of heated debate since its inception in medical education. Despite the many challenges and pitfalls of CBME that have been recognized by the medical education community, CBME is now seeing widespread implementation. However, the biggest problems with CBME still have not been solved. Two of these problems, reductionism and loss of authenticity, present major challenges when developing curricula and assessment tools.The authors address these problems by making a call for flexibility in competency definitions and for the use of mixed methods in CBME. First, they present the issue of reductionism and a similar concept from the field of data science, overfitting. Then they outline several solutions, both conceptual and concrete, to prevent undue reductionist tendencies in both competency definitions and in tools of assessment. Finally, they propose the reintroduction of qualitative methods to balance the historically quantitative emphasis of assessment in medical education.The authors maintain that mixed-methods assessment with multiple assessors in differing contexts can yield a more accurate representation of a medical trainee's skills and abilities, deter the loss of authenticity, and increase the willingness of medical educators to adopt a feasible form of CBME. Finally, they propose the deployment of dedicated faculty assessors and physician coaches (which will reduce training requirements for other faculty), as well as the use of formal qualitative tools of assessment alongside established quantitative tools, to encourage a truly mixed-methods approach to assessment.

摘要

基于能力的医学教育 (CBME) 自医学教育诞生以来一直是激烈争论的主题。尽管 CBME 已经被医学教育界认识到存在许多挑战和陷阱,但 CBME 现在正在广泛实施。然而,CBME 最大的问题仍然没有得到解决。其中两个问题,即简化论和真实性的丧失,在制定课程和评估工具时带来了重大挑战。

作者通过呼吁在能力定义方面具有灵活性,并在 CBME 中使用混合方法来解决这些问题。首先,他们提出了简化论的问题,以及数据科学领域的一个类似概念,即过度拟合。然后,他们概述了几种解决方案,包括概念性和具体性的解决方案,以防止在能力定义和评估工具中出现不必要的简化倾向。最后,他们提出重新引入定性方法,以平衡医学教育评估中历史上对定量方法的重视。

作者认为,在不同背景下使用多个评估者进行混合方法评估,可以更准确地反映医学受训者的技能和能力,防止真实性的丧失,并提高医学教育者采用可行的 CBME 形式的意愿。最后,他们建议部署专门的教师评估者和医师教练(这将降低对其他教师的培训要求),并结合使用正式的定性评估工具和已建立的定量工具,鼓励真正采用混合方法进行评估。

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