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Smart doctors and the three metaphors of learning

机译:聪明的医生和学习的三个隐喻

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The article by Odette Wegwarth and her colleagues describes conceptual tools to promote decision making in medical practice.1 They propose that there are two main ways of approaching decision making and clinical reasoning. The first, optimisation, involves attempts to integrate all available information in order to arrive at sound decisions. The second solution strategy is to apply heuristics, that is, rules of thumb that are based on less complete information. At the root of their argument lies the belief that adding increasing amounts of information can detract from the accuracy of the decisions made. This contention is so strongly counter to the intuitions of many that it is worth revisiting and reinforcing some of the reasons why this might be so, while also considering Wegwarth et a's argument in the context of other models of learning.
机译:Odette Wegwarth和她的同事的文章描述了在医学实践中促进决策制定的概念工具。1他们提出,有两种主要的决策制定方法和临床推理方法。第一个是优化,它涉及尝试整合所有可用信息以做出合理的决策。第二种解决方案策略是应用启发式方法,即基于不完整信息的经验法则。他们争论的根源在于,相信增加信息量会降低决策的准确性。这种论点与许多人的直觉强烈反驳,值得回顾和强调某些原因,同时在其他学习模式的背景下考虑韦格沃特等人的观点。

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