Collaborative coding in inductive content analysis: Why, when, and how to do it.
Inductive content analysis (ICA) is a useful method for analyzing qualitative data in genetic counseling research. It is particularly relevant when the goal is to examine and improve practices or develop recommendations. Although ICA can be undertaken by a single analyst, ideally there is involvemen...
| Publicado en: | Journal of Genetic Counseling Vol. 34; no. 3; pp. 1 - 13 |
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| Autores principales: | , , |
| Formato: | review tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Jun2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=186226442&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 186226442 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10597700 41A jtl: Journal of Genetic Counseling issn: 10597700 maglogo: N pubinfo: dt: Jun2025 vid: 34 iid: 3 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 186226442 186226442 186226442 10.1002/jgc4.70030 186226442 ppf: 1 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P tig: atl: Collaborative coding in inductive content analysis: Why, when, and how to do it. aug: au: Coulston, Free Lynch, Fiona Vears, Danya F. affil: The University of Melbourne, Parkville Victoria,, Australia sug: subj: Content Analysis Genetic Counseling Research, Medical Collaboration Paradigms Mentorship Research Personnel Genetic Screening Data Analytics Thematic Analysis Nomenclature ab: Inductive content analysis (ICA) is a useful method for analyzing qualitative data in genetic counseling research. It is particularly relevant when the goal is to examine and improve practices or develop recommendations. Although ICA can be undertaken by a single analyst, ideally there is involvement of multiple analysts (or co‐coders). Co‐coding can bring many benefits to qualitative analysis that sits within a constructivist paradigm, including developing a representation of the data that is not only understandable to more than one individual but also richer and more nuanced. It also provides an opportunity for mentoring more junior researchers and can be an efficient way to analyze large datasets. However, co‐coding requires important planning and consideration, and there is currently a paucity of clear guidance. In this paper, we provide an outline of the small body of existing literature on this topic and propose six flexible step‐by‐step components of our approach to co‐coding in ICA, based on our own work. We have utilized it to analyze reporting practices and perspectives for diagnostic genomic sequencing, informed consent for genetic testing, data sharing and storage, and genomic newborn screening, among other topics. To illustrate these components, we present some example vignettes to show how these procedures can be applied in different scenarios and with different analysts. pubtype: Academic Journal doctype: review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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