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It's all in whom you know (as well as what you know). Using network analysis and health professionals own knowledge for recruitment

Often the decision of whom to interview within a study is crucial to its ultimate success or failure, but when the study is to assess a very specific subset of health professionals, this decision may be even more vital.

One avenue that has been proposed within the AiMS Study, is to make use of the knowledge that the group of interest inherently possesses, the names of others working with the specific patient group. Using the tools of network analysis, the frequency and linkage of the names given will be analysed to assess who are the key or central persons whom the study should recruit for interview. Techniques will include degree, betweenness and eigenvector centrality, as while as core-periphery structure analysis of the network graph. The benefits of this approach are that rather than interviewing a large or random sample of health professionals, the core individuals are approached, reducing the number of interviews to be conducted, and in theory, conducting interviews with those having greater knowledge of the specific patient group. The protocol, ethical considerations and success of such a stratum for recruitment will be discussed.

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Open Access This article is published under license to BioMed Central Ltd. This is an Open Access article is distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( https://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated.

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McShane, C., Santin, O., McAneney, H. et al. It's all in whom you know (as well as what you know). Using network analysis and health professionals own knowledge for recruitment. Trials 14, P86 (2013). https://doi.org/10.1186/1745-6215-14-S1-P86

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  • DOI: https://doi.org/10.1186/1745-6215-14-S1-P86

Keywords

  • Public Health
  • Patient Group
  • Health Professional
  • Random Sample
  • Structure Analysis