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教授启发式和记忆术以提高鉴别诊断的生成能力。

Teaching heuristics and mnemonics to improve generation of differential diagnoses.

机构信息

Family Medicine, Wright State University Boonshoft School of Medicine (WSUBSOM), Fairborn, OH, USA.

PGY-1 Resident, Bethesda Hospital Family Medicine Residency Program, Cincinnati, OH, USA.

出版信息

Med Educ Online. 2020 Dec;25(1):1742967. doi: 10.1080/10872981.2020.1742967.

DOI:10.1080/10872981.2020.1742967
PMID:32182197
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7144285/
Abstract

: Differential diagnosis (DDx) is one of the key cognitive skills that medical learners must develop. However, little is known regarding the best methods for teaching DDx skills. As metacognition plays a fundamental role in the diagnostic process, we hypothesized that the teaching of specific heuristics and mnemonics collectively termed metamemory techniques (MMTs) would enhance the capacity of medical students to generate differential diagnoses.: In a 90-min DDx workshop, third-year medical students (N = 114) were asked to generate differentials before and after learning each of four MMTs. Differential sizes were compared using a linear mixed-effect model. Students also completed a post-session questionnaire which included a subjective ranking of the MMTs, as well as Likert-scale and free-text sections for course feedback.: One MMT (the , an anatomic visualization technique) significantly increased the size of student differentials (+13.3%, p =.0005). However, a marked cumulative increase across all four MMTs was noted (+36.5%, p <.0001). A majority of students ranked the the most useful MMT (51.5%). They found the workshop both worthwhile (4.51/5, CI 4.33-4.69) and enjoyable (4.33/5, CI 4.12-4.55), and considered the MMTs they learned useful and practical (4.49/5, CI 4.32-4.67).: The MMT-based DDx workshop was effective in enhancing the skill of DDx generation, and was rated very favorably by students.

摘要

鉴别诊断(DDx)是医学学习者必须发展的关键认知技能之一。然而,对于教授 DDx 技能的最佳方法知之甚少。由于元认知在诊断过程中起着基本作用,我们假设教授特定的启发式和助记符统称为元记忆技术(MMT)将提高医学生生成鉴别诊断的能力。

在一个 90 分钟的 DDx 研讨会上,三年级医学生(N=114)在学习了四种 MMT 中的每一种之前和之后都被要求生成鉴别诊断。使用线性混合效应模型比较鉴别诊断的大小。学生还完成了一次课后问卷调查,其中包括对 MMT 的主观排名,以及课程反馈的李克特量表和自由文本部分。

一种 MMT(the,一种解剖可视化技术)显著增加了学生鉴别诊断的大小(+13.3%,p=.0005)。然而,在所有四种 MMT 中都观察到了明显的累积增加(+36.5%,p<.0001)。大多数学生将 the 评为最有用的 MMT(51.5%)。他们认为研讨会既值得(4.51/5,CI 4.33-4.69)又有趣(4.33/5,CI 4.12-4.55),并认为他们所学的 MMT 既有用又实用(4.49/5,CI 4.32-4.67)。

基于 MMT 的 DDx 研讨会在提高 DDx 生成技能方面非常有效,并得到了学生的高度评价。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/acca/7144285/e0978d0da6f5/ZMEO_A_1742967_F0001_B.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/acca/7144285/e0978d0da6f5/ZMEO_A_1742967_F0001_B.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/acca/7144285/e0978d0da6f5/ZMEO_A_1742967_F0001_B.jpg

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