透明度(行为)
医学教育
心理干预
基于标准的评估
协同生产
教育评估
医学
心理学
计算机科学
护理部
公共关系
政治学
计算机安全
作者
Kayla Marcotte,Jose A. Negrete Manriquez,Maya Hunt,Maxwell Spadafore,Kenneth H. Perrone,Christine Zhou
出处
期刊:Academic Medicine
[Ovid Technologies (Wolters Kluwer)]
日期:2023-12-18
卷期号:99 (4S): S25-S29
被引量:6
标识
DOI:10.1097/acm.0000000000005602
摘要
Abstract The next era of assessment in medical education promises new assessment systems, increased focus on ensuring high-quality equitable patient care, and precision education to drive learning and improvement. The potential benefits of using learning analytics and technology to augment medical training abound. To ensure that the ideals of this future for medical education are realized, educators should partner with trainees to build and implement new assessment systems. Coproduction of assessment systems by educators and trainees will help to ensure that new educational interventions are feasible and sustainable. In this paper, the authors provide a trainee perspective on 5 key areas that affect trainees in the next era of assessment: (1) precision education, (2) assessor education, (3) transparency in assessment development and implementation, (4) ongoing evaluation of the consequences of assessment, and (5) patient care data as sources of education outcomes. As precision education is developed, it is critical that trainees understand how their educational data are collected, stored, and ultimately utilized for educational outcomes. Since assessors play a key role in generating assessment data, it is important that they are prepared to give high-quality assessments and are continuously evaluated on their abilities. Transparency in the development and implementation of assessments requires communicating how assessments are created, the evidence behind them, and their intended uses. Furthermore, ongoing evaluation of the intended and unintended consequences that new assessments have on trainees should be conducted and communicated to trainees. Finally, trainees should participate in determining what patient care data are used to inform educational outcomes. The authors believe that trainee coproduction is critical to building stronger assessment systems that utilize evidence-based educational theories for improved learning and ultimately better patient care.
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