Research Output
Understanding complex interactions using social network analysis
  Aims and objectives. The aim of this paper is to raise the awareness of social network analysis as a method to facilitate research in nursing research.
Background. The application of social network analysis in assessing network properties has allowed greater insight to be gained in many areas including sociology, politics, business organisation and health care. However, the use of social networks in nursing has not received sufficient attention.
Design. Review of literature and illustration of the application of the method of social network analysis using research
Methods. First, the value of social networks will be discussed. Then by using illustrative examples, the value of social network analysis to nursing will be demonstrated.
Results. The method of social network analysis is found to give greater insights into social situations involving interactions
between individuals and has particular application to the study of interactions between nurses and between nurses and patients and other actors.
Conclusion. Social networks are systems in which people interact. Two quantitative techniques help our understanding of these networks. The first is visualisation of the network. The second is centrality. Individuals with high centrality are key communicators in a network. Relevance to clinical practice. Applying social network analysis to nursing provides a simple method that helps gain an understanding of human interaction and how this might influence various health outcomes. It allows influential individuals (actors) to be identified. Their influence on the formation of social norms and communication can determine the extent to which
new interventions or ways of thinking are accepted by a group. Thus, working with key individuals in a network could be critical to the success and sustainability of an intervention. Social network analysis can also help to assess the effectiveness of such interventions for the recipient and the service provider..

  • Type:


  • Date:

    31 October 2012

  • Publication Status:


  • Publisher

    Blackwell Synergy

  • DOI:


  • ISSN:


  • Library of Congress:

    RT Nursing


Pow, J. S., Gayen, K., Elliott, L., & Raeside, R. (2012). Understanding complex interactions using social network analysis. Journal of Clinical Nursing, 21, 2772-2779.



Complex interventions; logistic regression; nursing; social networks; statistics

Monthly Views:

Available Documents