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Network methods to support user involvement in qualitative data analyses: an introduction to Participatory Theme Elicitation

Overview of attention for article published in Trials, November 2017
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Title
Network methods to support user involvement in qualitative data analyses: an introduction to Participatory Theme Elicitation
Published in
Trials, November 2017
DOI 10.1186/s13063-017-2289-5
Pubmed ID
Authors

Paul Best, Jennifer Badham, Rekesh Corepal, Roisin F. O’Neill, Mark A. Tully, Frank Kee, Ruth F. Hunter

Abstract

While Patient and Public Involvement (PPI) is encouraged throughout the research process, engagement is typically limited to intervention design and post-analysis stages. There are few approaches to participatory data analyses within complex health interventions. Using qualitative data from a feasibility randomised controlled trial (RCT), this proof-of-concept study tests the value of a new approach to participatory data analysis called Participatory Theme Elicitation (PTE). Forty excerpts were given to eight members of a youth advisory PPI panel to sort into piles based on their perception of related thematic content. Using algorithms to detect communities in networks, excerpts were then assigned to a thematic cluster that combined the panel members' perspectives. Network analysis techniques were also used to identify key excerpts in each grouping that were then further explored qualitatively. While PTE analysis was, for the most part, consistent with the researcher-led analysis, young people also identified new emerging thematic content. PTE appears promising for encouraging user led identification of themes arising from qualitative data collected during complex interventions. Further work is required to validate and extend this method. ClinicalTrials.gov, ID: NCT02455986 . Retrospectively Registered on 21 May 2015.

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The data shown below were compiled from readership statistics for 125 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 125 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 10%
Student > Master 13 10%
Student > Bachelor 11 9%
Student > Doctoral Student 10 8%
Researcher 8 6%
Other 19 15%
Unknown 51 41%
Readers by discipline Count As %
Medicine and Dentistry 19 15%
Nursing and Health Professions 16 13%
Social Sciences 9 7%
Psychology 9 7%
Sports and Recreations 5 4%
Other 11 9%
Unknown 56 45%