Peeters Michael J
University of Toledo College of Pharmacy & Pharmaceutical Sciences.
Innov Pharm. 2021 Feb 26;12(1). doi: 10.24926/iip.v12i1.2131. eCollection 2021.
When available, empirical evidence should help guide decision-making. Following each administration of a learning assessment, data becomes available for analysis. For learning assessments, Kane's Framework for Validation can helpfully categorize evidence by inference (i.e., scoring, generalization, extrapolation, implications). Especially for test-scores used within a high-stakes setting, generalization evidence is critical. While reporting Cronbach's alpha, inter-rater reliability, and other reliability coefficients for a single measurement error are somewhat common in pharmacy education, dealing with multiple concurrent sources of measurement error within complex learning assessments is not. Performance-based assessments (e.g., OSCEs) that use raters, are inherently complex learning assessments.
Generalizability Theory (G-Theory) can account for multiple sources of measurement error. G-Theory is a powerful tool that can provide a composite reliability (i.e., generalization evidence) for more complex learning assessments, including performance-based assessments. It can also help educators explore ways to make a learning assessment more rigorous if needed, as well as suggest ways to better allocate resources (e.g., staffing, space, fiscal). A brief review of G-Theory is discussed herein focused on pharmacy education.
G-Theory has been common and useful in medical education, though has been used rarely in pharmacy education. Given the similarities in assessment methods among health-professions, G-Theory should prove helpful in pharmacy education as well. Within this Journal and accompanying this Idea Paper, there are multiple reports that demonstrate use of G-Theory in pharmacy education.
如有实证依据,应有助于指导决策。每次进行学习评估后,就会有可供分析的数据。对于学习评估,凯恩验证框架可按推理(即评分、泛化、外推、影响)对证据进行有益分类。特别是对于在高风险环境中使用的考试分数,泛化证据至关重要。虽然在药学教育中报告克朗巴赫α系数、评分者间信度和其他单一测量误差的信度系数较为常见,但处理复杂学习评估中多个并发测量误差来源的情况却不常见。使用评分者的基于表现的评估(如客观结构化临床考试)本质上是复杂的学习评估。
概化理论(G理论)可以解释多个测量误差来源。G理论是一种强大的工具,可以为更复杂的学习评估(包括基于表现的评估)提供综合信度(即泛化证据)。它还可以帮助教育工作者探索在需要时使学习评估更严格的方法,并建议更好地分配资源(如人员配备、空间、资金)的方法。本文将简要回顾聚焦于药学教育的G理论。
G理论在医学教育中一直很常见且有用,尽管在药学教育中很少使用。鉴于健康专业之间评估方法的相似性,G理论在药学教育中也应会有所帮助。在本期刊以及随附的这篇观点论文中,有多篇报告展示了G理论在药学教育中的应用。