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医学生的推理策略与生物医学知识的运用

Reasoning strategies and the use of biomedical knowledge by medical students.

作者信息

Patel V L, Evans D A, Kaufman D R

机构信息

Cognitive Studies in Medicine, McGill University, Montreal, Quebec, Canada.

出版信息

Med Educ. 1990 Mar;24(2):129-36. doi: 10.1111/j.1365-2923.1990.tb02511.x.

Abstract

This paper reports one of a series of studies conducted to investigate the role of biomedical knowledge in clinical reasoning. It was motivated by findings from our earlier studies that demonstrate that when specific basic science information is provided to medical students prior to solving a clinical case, they are unable to use this information in explaining the patient problem. An experiment was designed to investigate the use of biomedical information in the explanation of a clinical problem without any basic science information (spontaneous explanation) and where basic science information was provided after the clinical case (biomedically primed explanation). The results are discussed in the context of a two-stage model of diagnostic reasoning. The first stage is referred to as data-driven reasoning, and is characterized by the triggering of inferences from observations in the data to hypotheses. The second stage is designated as predictive reasoning, and is characterized by the generation of inferences driven by hypotheses. The results show that, with the exception of final-year medical students, the use of biomedical information interfered with the data-driven reasoning process. However, it did facilitate the process of predictive reasoning by the students. It is proposed that a sound disease classification scheme is necessary before biomedical knowledge can facilitate both data-driven and predictive reasoning during clinical problem-solving.

摘要

本文报告了一系列旨在研究生物医学知识在临床推理中作用的研究之一。其动机源于我们早期研究的结果,这些结果表明,当在解决临床病例之前向医学生提供特定的基础科学信息时,他们无法利用这些信息来解释患者的问题。设计了一项实验,以研究在没有任何基础科学信息的情况下(自发解释)以及在临床病例之后提供基础科学信息的情况下(生物医学启动解释)生物医学信息在解释临床问题中的使用情况。在诊断推理的两阶段模型的背景下讨论了结果。第一阶段称为数据驱动推理,其特征是从数据中的观察结果触发到假设的推理。第二阶段称为预测推理,其特征是由假设驱动的推理生成。结果表明,除了即将毕业的医学生外,生物医学信息的使用干扰了数据驱动的推理过程。然而,它确实促进了学生的预测推理过程。有人提出,在生物医学知识能够促进临床问题解决过程中的数据驱动和预测推理之前,一个完善的疾病分类方案是必要的。

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