Dados Bibliográficos

AUTOR(ES) J. Lee , Carol S. Camlin , Jason Johnson-Peretz , Titus O. Arunga , Cecilia Akatukwasa , Fredrick Atwine , Angeline Onyango , Lawrence Owino
AFILIAÇÃO(ÕES) Department of Obstetrics, Gynecology & Reproductives Sciences, University of California, San Francisco (UCSF), San Francisco, CA, USA, Kenya Medical Research Institute (KEMRI), Kisumu, Kenya, Infectious Diseases Research Collaboration, Kampala, Uganda
ANO 2024
TIPO Artigo
PERIÓDICO International Journal of Qualitative Methods
ISSN 1609-4069
E-ISSN 1609-4069
DOI 10.1177/16094069241236268
ADICIONADO EM 2025-08-18

Resumo

Qualitative methods encompass a variety of research and analysis techniques which have the common aim of uncovering what cannot be captured numerically through the quantification of data. For qualitative analytical methods in the interpretivist tradition (e.g. grounded theory, phenomenological, thematic, etc), inductive coding has become a mainstay but has not always lent itself to collaborative, remote team-based data interpretation among qualitative and mixed-methods clinical researchers. Finding ways to speed the inductive coding process without sacrificing rigour while remaining accessible to geographically dispersed teams remains a priority. This is especially crucial in global health partnerships where on-the-ground researchers may have less input into codebook development compared to in-the-office researchers. We describe a newly-developed, digital approach that integrates findings from our qualitative team, which we call R-EIGHT (Remote and Equitable Inductive Analysis for Global Health Teams). The technique we developed a) speeds the process of inductive coding as a team, b) visually displays interpretive consensus, and c) when appropriate fosters streamlined integration of inductive findings into codebooks. Because it involves all team members, our approach helps break the divide between in-office and on-the-ground teams, fostering integrated and representative contributions from all globally-dispersed team members.

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