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Using the structure of social networks to map inter-agency relationships in public health services

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  • West, Robert M.
  • House, Allan O.
  • Keen, Justin
  • Ward, Vicky L.

Abstract

This article investigates network governance in the context of health and wellbeing services in England, focussing on relationships between managers in a range of services. There are three aims, namely to investigate, (i) the configurations of networks, (ii) the stability of network relationships over time and, (iii) the balance between formal and informal ties that underpin inter-agency relationships. Latent position cluster network models were used to characterise relationships. Managers were asked two questions, both designed to characterise informal relationships. The resulting networks differed substantially from one another in membership. Managers described networks of relationships that spanned organisational boundaries, and that changed substantially over time. The findings suggest that inter-agency co-ordination depends more on informal than on formal relationships.

Suggested Citation

  • West, Robert M. & House, Allan O. & Keen, Justin & Ward, Vicky L., 2015. "Using the structure of social networks to map inter-agency relationships in public health services," Social Science & Medicine, Elsevier, vol. 145(C), pages 107-114.
  • Handle: RePEc:eee:socmed:v:145:y:2015:i:c:p:107-114
    DOI: 10.1016/j.socscimed.2015.10.002
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    References listed on IDEAS

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    1. Provan, Keith G. & Leischow, Scott J. & Keagy, Judith & Nodora, Jesse, 2010. "Research collaboration in the discovery, development, and delivery networks of a statewide cancer coalition," Evaluation and Program Planning, Elsevier, vol. 33(4), pages 349-355, November.
    2. Lanham, Holly Jordan & Leykum, Luci K. & Taylor, Barbara S. & McCannon, C. Joseph & Lindberg, Curt & Lester, Richard T., 2013. "How complexity science can inform scale-up and spread in health care: Understanding the role of self-organization in variation across local contexts," Social Science & Medicine, Elsevier, vol. 93(C), pages 194-202.
    3. Hoff P.D. & Raftery A.E. & Handcock M.S., 2002. "Latent Space Approaches to Social Network Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1090-1098, December.
    4. Amin, Ash & Roberts, Joanne, 2008. "Knowing in action: Beyond communities of practice," Research Policy, Elsevier, vol. 37(2), pages 353-369, March.
    5. Lewis, Jenny M. & Baeza, Juan I. & Alexander, Damon, 2008. "Partnerships in primary care in Australia: Network structure, dynamics and sustainability," Social Science & Medicine, Elsevier, vol. 67(2), pages 280-291, July.
    6. Clark, Alexander M., 2013. "What are the components of complex interventions in healthcare? Theorizing approaches to parts, powers and the whole intervention," Social Science & Medicine, Elsevier, vol. 93(C), pages 185-193.
    7. Trenholm, Susan & Ferlie, Ewan, 2013. "Using complexity theory to analyse the organisational response to resurgent tuberculosis across London," Social Science & Medicine, Elsevier, vol. 93(C), pages 229-237.
    8. Mark S. Handcock & Adrian E. Raftery & Jeremy M. Tantrum, 2007. "Model‐based clustering for social networks," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(2), pages 301-354, March.
    9. Salter-Townshend, Michael & Murphy, Thomas Brendan, 2013. "Variational Bayesian inference for the Latent Position Cluster Model for network data," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 661-671.
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    Cited by:

    1. Tobias Fleuren & Ansgar Thiel & Annika Frahsa, 2021. "Identification of Network Promoters in a Regional and Intersectoral Health Promotion Network: A Qualitative Social Network Analysis in Southern Germany," IJERPH, MDPI, vol. 18(16), pages 1-15, August.

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