Centre for Health Informatics, Australian Institute of Health Innovation, University of New South Wales Sydney, NSW, Australia.
Soc Sci Med. 2011 Apr;72(7):1064-8. doi: 10.1016/j.socscimed.2011.01.029. Epub 2011 Feb 15.
Social network analysis is an increasingly popular sociological method used to describe and understand the social aspects of communication patterns in the health care sector. The networks studied in this area are special because they are small, and for these sizes, the metrics calculated during analysis are sensitive to the number of people in the network and the density of observed communication. Validation is of particular value in controlling for these factors and in assisting in the accurate interpretation of network findings, yet such approaches are rarely applied. Our aim in this paper was to bring together published case studies to demonstrate how a proposed validation technique provides a basis for standardised comparison of networks within and across studies. A validation is performed for three network studies comprising ten networks, where the results are compared within and across the studies in relation to a standard baseline. The results confirm that hierarchy, centralisation and clustering metrics are highly sensitive to changes in size or density. Amongst the three case studies, we found support for some conclusions and contrary evidence for others. This validation approach is a tool for identifying additional features and verifying the conclusions reached in observational studies of small networks. We provide a methodological basis from which to perform intra-study and inter-study comparisons, for the purpose of introducing greater rigour to the use of social network analysis in health care applications.
社会网络分析是一种越来越流行的社会学方法，用于描述和理解医疗保健领域中沟通模式的社会方面。该领域研究的网络具有特殊性，因为它们规模较小，而在分析过程中计算的指标对网络中的人数和观察到的通信密度很敏感。验证对于控制这些因素和准确解释网络结果特别有价值，但这种方法很少被应用。我们在本文中的目的是汇集已发表的案例研究，展示所提出的验证技术如何为在研究内和跨研究中标准化比较网络提供基础。我们对包含十个网络的三个网络研究进行了验证，结果与标准基线进行了比较。结果证实，层次结构、集中化和聚类指标对大小或密度的变化非常敏感。在这三个案例研究中，我们发现一些结论得到了支持，而其他结论则与之相悖。这种验证方法是一种用于识别附加特征和验证小网络观察研究中得出结论的工具。我们提供了一种方法学基础，以便在医疗保健应用中进行内部和跨研究比较，从而提高社会网络分析的使用严谨性。
