Woods Nicole N, Brooks Lee R, Norman Geoffrey R
Department of Surgery, The Wilson Centre, University of Toronto, Toronto, ON, Canada M5G 2C4.
Adv Health Sci Educ Theory Pract. 2007 Nov;12(4):405-15. doi: 10.1007/s10459-006-9055-x. Epub 2007 Feb 22.
Although there is consensus among medical educators that students must receive training in the biomedical sciences, little is known regarding the role of biomedical knowledge in diagnosis. The present paper presents two studies examining the role of biomedical knowledge, specifically knowledge of causal mechanisms, in novice diagnosticians. In Experiment 1, two groups of participants are taught to diagnose a series of artificial diseases. In the causal learning condition students learn the underlying causal mechanisms for each feature. A second group learns the same diseases without the causal explanations. Participants are asked to diagnose a series of written cases immediately after training and again 1 week later. The results show that students who learn a causal model are better able to retain their diagnostic performance over time (89% correct vs. 78%). This finding is investigated further in Experiment 2, demonstrating that students rely more on casual information after a delay (mean probability of 57% vs. 43%). Together, the studies suggest that knowledge of underlying causal mechanisms can aid student memory for diagnostic categories and that use of causal knowledge changes over time.
尽管医学教育工作者们一致认为学生必须接受生物医学科学方面的培训,但对于生物医学知识在诊断中的作用却知之甚少。本文介绍了两项研究,探讨生物医学知识,特别是因果机制知识在新手诊断医生中的作用。在实验1中,两组参与者被教导诊断一系列人工疾病。在因果学习条件下,学生学习每个特征的潜在因果机制。另一组在没有因果解释的情况下学习相同的疾病。参与者在训练后立即被要求诊断一系列书面病例,并在1周后再次进行诊断。结果表明,学习因果模型的学生能够随着时间的推移更好地保持他们的诊断表现(正确率89%对78%)。在实验2中对这一发现进行了进一步研究,结果表明,经过一段时间后,学生更多地依赖因果信息(平均概率为57%对43%)。综合来看,这些研究表明,潜在因果机制的知识可以帮助学生记忆诊断类别,并且因果知识的使用会随着时间而变化。