Embedding Big Qual and Team Science Into Qualitative Research: Lessons From a Large-Scale, Cross-Site Research Study
Dados Bibliográficos
| AUTOR(ES) | |
|---|---|
| AFILIAÇÃO(ÕES) | University of Kentucky, Columbia University Irving Medical Center, The Ohio State University, Boston University |
| ANO | 2023 |
| TIPO | Artigo |
| PERIÓDICO | International Journal of Qualitative Methods |
| ISSN | 1609-4069 |
| E-ISSN | 1609-4069 |
| DOI | 10.1177/16094069231165933 |
| ADICIONADO EM | 2025-08-18 |
Resumo
Background: A major part of the HEALing Communities Study (HCS), launched in 2019 to address the growing opioid epidemic, is evaluating the study's intervention implementation process through an implementation science (IS) approach. One component of the IS approach involves teams with more than 20 researchers collaborating across four research sites to conduct in-depth qualitative interviews with over 300 participants at four time points. After completion of the first two rounds of data collection, we reflect upon our qualitative data collection and analysis approach. We aim to share our lessons learned about designing and applying qualitative methods within an implementation science framework. Methods: The HCS evaluation is based on the RE-AIM/PRISM framework and incorporates interviews at four timepoints. At each timepoint, the core qualitative team of the Intervention Work Group drafts an interview guide based on the framework and insights from previous round(s) of data collection. Researchers then conduct interviews with key informants and coalition members within their respective states. Data analysis involves drafting, iteratively refining, and finalizing a codebook in a cross-site and within-site consensus processes. Interview transcripts are then individually coded by researchers within their respective states. Results: Successes in the evaluation process include having structured procedures for communication, data collection, and analysis, all of which are critical for ensuring consistent data collection and for achieving consensus during data analysis. Challenges include recognizing and accommodating the diversity of training and knowledge among researchers, as well as establishing reliable ways to securely store, manage, and share the large volumes of data. Conclusion: Qualitative methods using a Team Science approach have been limited in their application in large, multi-site randomized controlled trials of health interventions. Our experience provides practical guidance for future studies with large teams that are experientially and disciplinarily diverse and that are seeking to incorporate qualitative or mixed-methods components for their evaluations.