102
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

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

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          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.

          Methods

          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.

          Results

          While PTE analysis was, for the most part, consistent with the researcher-led analysis, young people also identified new emerging thematic content.

          Conclusions

          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.

          Trial registration

          ClinicalTrials.gov, ID: NCT02455986. Retrospectively Registered on 21 May 2015.

          Related collections

          Most cited references28

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Community structure in social and biological networks

          A number of recent studies have focused on the statistical properties of networked systems such as social networks and the World-Wide Web. Researchers have concentrated particularly on a few properties which seem to be common to many networks: the small-world property, power-law degree distributions, and network transitivity. In this paper, we highlight another property which is found in many networks, the property of community structure, in which network nodes are joined together in tightly-knit groups between which there are only looser connections. We propose a new method for detecting such communities, built around the idea of using centrality indices to find community boundaries. We test our method on computer generated and real-world graphs whose community structure is already known, and find that it detects this known structure with high sensitivity and reliability. We also apply the method to two networks whose community structure is not well-known - a collaboration network and a food web - and find that it detects significant and informative community divisions in both cases.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            GRIPP2 reporting checklists: tools to improve reporting of patient and public involvement in research

            Background While the patient and public involvement (PPI) evidence base has expanded over the past decade, the quality of reporting within papers is often inconsistent, limiting our understanding of how it works, in what context, for whom, and why. Objective To develop international consensus on the key items to report to enhance the quality, transparency, and consistency of the PPI evidence base. To collaboratively involve patients as research partners at all stages in the development of GRIPP2. Methods The EQUATOR method for developing reporting guidelines was used. The original GRIPP (Guidance for Reporting Involvement of Patients and the Public) checklist was revised, based on updated systematic review evidence. A three round Delphi survey was used to develop consensus on items to be included in the guideline. A subsequent face-to-face meeting produced agreement on items not reaching consensus during the Delphi process. Results One hundred forty-three participants agreed to participate in round one, with an 86% (123/143) response for round two and a 78% (112/143) response for round three. The Delphi survey identified the need for long form (LF) and short form (SF) versions. GRIPP2-LF includes 34 items on aims, definitions, concepts and theory, methods, stages and nature of involvement, context, capture or measurement of impact, outcomes, economic assessment, and reflections and is suitable for studies where the main focus is PPI. GRIPP2-SF includes five items on aims, methods, results, outcomes, and critical perspective and is suitable for studies where PPI is a secondary focus. Conclusions GRIPP2-LF and GRIPP2-SF represent the first international evidence based, consensus informed guidance for reporting patient and public involvement in research. Both versions of GRIPP2 aim to improve the quality, transparency, and consistency of the international PPI evidence base, to ensure PPI practice is based on the best evidence. In order to encourage its wide dissemination this article is freely accessible on The BMJ and Research Involvement and Engagement journal websites. Electronic supplementary material The online version of this article (doi:10.1186/s40900-017-0062-2) contains supplementary material, which is available to authorized users.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Supporting thinking on sample sizes for thematic analyses: a quantitative tool

                Bookmark

                Author and article information

                Contributors
                p.best@qub.ac.uk
                j.badham@qub.ac.uk
                rcorepal@qub.ac.uk
                roneill@qub.ac.uk
                m.tully@qub.ac.uk
                f.kee@qub.ac.uk
                ruth.hunter@qub.ac.uk
                Journal
                Trials
                Trials
                Trials
                BioMed Central (London )
                1745-6215
                23 November 2017
                23 November 2017
                2017
                : 18
                : 559
                Affiliations
                [1 ]ISNI 0000 0004 0374 7521, GRID grid.4777.3, School of Social Sciences, Education and Social Work, , Queen’s University, ; Belfast, UK
                [2 ]ISNI 0000 0004 0374 7521, GRID grid.4777.3, School of Medicine, Dentistry and Biomedical Sciences, , Queen’s University, ; Belfast, UK
                Author information
                http://orcid.org/0000-0001-6947-8916
                http://orcid.org/0000-0002-4171-3897
                Article
                2289
                10.1186/s13063-017-2289-5
                5701364
                29169378
                0b58babb-8e0e-478d-b047-17a017bc7bb3
                © The Author(s). 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 17 May 2017
                : 24 October 2017
                Funding
                Funded by: HSC R&D Office (NI) Enabling Research Award
                Categories
                Methodology
                Custom metadata
                © The Author(s) 2017

                Medicine
                network analysis,participatory analysis,user involvement,trials,patient and public involvement

                Comments

                Comment on this article

                scite_
                25
                0
                40
                0
                Smart Citations
                25
                0
                40
                0
                Citing PublicationsSupportingMentioningContrasting
                View Citations

                See how this article has been cited at scite.ai

                scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.

                Similar content60

                Cited by8