Broadening conceptions of learning in medical education: the message from teamworking
Broadening conceptions of learning in medical education: the message from teamworking
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DOI：
10.1111/j.1365-2929.2005.02371.x
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发表时间：
2006-02-01
影响因子：
6
通讯作者：
Bleakley, A
中科院分区：
文献类型：
--
作者：
Bleakley, A
背景：在广泛的教育文献中提供的广泛的学习理论和在医学教育文献中享有特权的相对狭窄的理论之间存在不匹配。后者通常在“成人学习理论”的标题下进行描述。方法：本文批判性地阐述了当前主流学习理论对医学教育的局限性。有一种观点认为，这些理论解决了个人如何学习的问题，却无法解释学习是如何在动态、复杂和不稳定的系统中发生的，比如流动的临床团队。考虑到分布式认知、时间和空间的学习以及学习环境的复杂性（包括人与人工制品之间的关系）的学习模型，在解释和预测临床团队中学习如何发生方面更强大。由于意识形态的原因，学习理论可能会受到优待，比如医学对自主性的关注。结论：当越来越多的医学教育发生在工作场所时，社会文化学习理论为这种学习提供了最合适的探索和解释。我们需要继续开发可测试的学习模型，为安全工作实践提供信息。一种学习理论不会适用于所有的实践环境，我们需要考虑一系列在实践中可测试的适合目的的理论。当前令人兴奋的发展包括基于复杂性理论的动态学习模型。
BACKGROUND There is a mismatch between the broad range of learning theories offered in the wider education literature and a relatively narrow range of theories privileged in the medical education literature. The latter are usually described under the heading of 'adult learning theory'.METHODS This paper critically addresses the limitations of the current dominant learning theories informing medical education. An argument is made that such theories, which address how an individual learns, fail to explain how learning occurs in dynamic, complex and unstable systems such as fluid clinical teams.RESULTS Models of learning that take into account distributed knowing, learning through time as well as space, and the complexity of a learning environment including relationships between persons and artefacts, are more powerful in explaining and predicting how learning occurs in clinical teams. Learning theories may be privileged for ideological reasons, such as medicine's concern with autonomy.CONCLUSIONS Where an increasing amount of medical education occurs in workplace contexts, sociocultural learning theories offer a best-fit exploration and explanation of such learning. We need to continue to develop testable models of learning that inform safe work practice. One type of learning theory will not inform all practice contexts and we need to think about a range of fit-for-purpose theories that are testable in practice. Exciting current developments include dynamicist models of learning drawing on complexity theory.