Official websites use. Share sensitive information only on official, secure websites. Community-based participatory research is a growing approach, but often includes higher levels of community engagement in the research design and data collection stages than in the data interpretation stage. Involving study participants in this stage could further knowledge justice, science that aligns with and supports social justice agendas. This article reports on two community-based participatory environmental health surveys conducted between and in an industrial region near Marseille, France, and focuses specifically on our approach of organizing focus groups to directly involve residents and community stakeholders in the analysis and interpretation process.
We found that, in these focus groups, residents triangulated across many different sources of informationβstudy findings, local knowledge, and different types of expert knowledgeβto reach conclusions about the health of their community and make recommendations for what should be done to improve community health outcomes. We conclude that involving residents in the data analysis and interpretation stage can promote epistemic justice and lead to final reports that are more useful to community stakeholders and decision-makers.
Keywords: community-based participatory research, data interpretation, environmental health, knowledge justice, public health, participatory science. This article documents a case study of one way in which community-based participatory research can lead to more meaningful research by engaging community members deeply in data analyses and interpretation. This also has implications for the use of collaboratively created research evidence for broader public awareness and potential policy impacts in promoting environmental health [ 2 ].
One of the key principles of CBPR is attention to collaboration with communities throughout all phases of the research process from problem definition and data collection to data analysis and interpretation and, hopefully, action [ 1 ].
As has been pointed out by Binet et al. The reasons for this range from longer timeframes, funding inflexibility, and institutional cultures, to lack of instructive examples on collaborative data analysis and interpretation [ 3 ]. Our research is an attempt to address the last issue by providing not only, a clear description of how we conducted collaborative analysis and interpretation, but also explain and illustrate why this last step is important.
