Department of Pediatrics, Wayne State University, Detroit, Michigan, United States of America.
Department of Epileptology, Tohoku University Graduate School of Medicine, Sendai, Japan.
PLoS One. 2022 Jan 27;17(1):e0263182. doi: 10.1371/journal.pone.0263182. eCollection 2022.
Given scientific and technological advancements, expectations of online medical education are increasing. However, there is no way to predict the effectiveness of online clinical clerkship curricula. To develop a prediction model, we conducted cross-sectional national surveys in Japan. Social media surveys were conducted among medical students in Japan during the periods May-June 2020 and February-March 2021. We used the former for the derivation dataset and the latter for the validation dataset. We asked students questions in three areas: 1) opportunities to learn from each educational approach (lectures, medical quizzes, assignments, oral presentations, observation of physicians' practice, clinical skills practice, participation in interprofessional meetings, and interactive discussions with physicians) in online clinical clerkships compared to face-to-face, 2) frequency of technical problems on online platforms, and 3) satisfaction and motivation as outcome measurements. We developed a scoring system based on a multivariate prediction model for satisfaction and motivation in a cross-sectional study of 1,671 medical students during the period May-June 2020. We externally validated this scoring with a cross-sectional study of 106 medical students during February-March 2021 and assessed its predictive performance. The final prediction models in the derivation dataset included eight variables (frequency of lectures, medical quizzes, oral presentations, observation of physicians' practice, clinical skills practice, participation in interprofessional meetings, interactive discussions with physicians, and technical problems). We applied the prediction models created using the derivation dataset to a validation dataset. The prediction performance values, based on the area under the receiver operating characteristic curve, were 0.69 for satisfaction (sensitivity, 0.50; specificity, 0.89) and 0.75 for motivation (sensitivity, 0.71; specificity, 0.85). We developed a prediction model for the effectiveness of the online clinical clerkship curriculum, based on students' satisfaction and motivation. Our model will accurately predict and improve the online clinical clerkship curriculum effectiveness.
随着科学技术的进步，人们对在线医学教育的期望越来越高。然而，目前还无法预测在线临床实习课程的效果。为了开发一个预测模型，我们在日本进行了横断面全国性调查。2020 年 5 月至 6 月和 2021 年 2 月至 3 月期间，我们在日本的医学生中进行了社交媒体调查。我们使用前者作为推导数据集，后者作为验证数据集。我们向学生提出了三个领域的问题：1）与面对面相比，在线临床实习中从每种教育方法（讲座、医学测验、作业、口头报告、观察医生的实践、临床技能实践、参与多专业会议和与医生的互动讨论）中学习的机会；2）在线平台上技术问题的频率；3）作为结果测量的满意度和动机。我们根据 2020 年 5 月至 6 月期间对 1671 名医学生进行的横断面研究中的满意度和动机的多变量预测模型，开发了一个评分系统。我们使用 2021 年 2 月至 3 月期间对 106 名医学生进行的横断面研究对该评分进行了外部验证，并评估了其预测性能。推导数据集中的最终预测模型包括八个变量（讲座频率、医学测验、口头报告、观察医生的实践、临床技能实践、参与多专业会议、与医生的互动讨论和技术问题）。我们将在推导数据集中创建的预测模型应用于验证数据集。基于接受者操作特征曲线下的面积，预测性能值为满意度 0.69（灵敏度为 0.50，特异性为 0.89）和动机 0.75（灵敏度为 0.71，特异性为 0.85）。我们基于学生的满意度和动机，为在线临床实习课程的效果开发了一个预测模型。我们的模型将准确预测并提高在线临床实习课程的效果。
