학술논문
Embedding Big Qual and Team Science into Qualitative Research: Lessons from a Large-scale, Cross-site Research Study.
Document Type
Academic Journal
Author
McAlearney AS; Ohio State University.; Walker DM; Ohio State University.; Shiu-Yee K; Ohio State University.; Crable EL; Boston University.; Auritt V; Boston Medical Center.; Barkowski L; Boston Medical Center.; Batty EJ; University of Kentucky.; Dasgupta A; Columbia University.; Goddard-Eckrich D; Columbia University.; Knudsen HK; University of Kentucky.; McCrimmon T; Columbia University.; Olvera R; Ohio State University.; Scalise A; Boston Medical Center.; Sieck C; Ohio State University.; Wood J; University of Kentucky.; Drainoni ML; Boston University.
Source
Publisher: SAGE Publications Country of Publication: United States NLM ID: 101213922 Publication Model: Print-Electronic Cited Medium: Print ISSN: 1609-4069 (Print) Linking ISSN: 16094069 NLM ISO Abbreviation: Int J Qual Methods Subsets: PubMed not MEDLINE
Subject
Language
English
ISSN
1609-4069
Abstract
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 includes 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 between researchers, and 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, experientially and disciplinarily diverse teams, and teams seeking to incorporate qualitative or mixed-methods components for their evaluations.
Competing Interests: Conflict of Interest The authors have no conflicts of interest to declare.
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 includes 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 between researchers, and 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, experientially and disciplinarily diverse teams, and teams seeking to incorporate qualitative or mixed-methods components for their evaluations.
Competing Interests: Conflict of Interest The authors have no conflicts of interest to declare.