Health and Wellbeing are Foundational for Successful Reentry: Part II – Lived Experience

Part II:

 At Nehemiah, we are uniquely positioned between communities and systems. Our research team intentionally designs research questions and studies that challenge traditional academic approaches that have widened the historic gap between research, reality, and practice. Instead of starting with abstract theories, we ground our inquiries in lived experience, to produce “findings” that are practical and useful for the communities most affected. 

Our approach to “research design” starts with a simple idea:  the understanding that community-engaged research is just as much coalition-building as it is knowledge-building. In our present system, credentialed experts (those trained in academic institutions) have little to no communication with experiential experts (those trained by their lived experience). This research project challenges that norm by putting stakeholders in conversation with each other and with people who have power to make change. The outcome? A relationship beyond pure “transaction.” Instead of structures that treat communities as data sources, we cultivate collaboration as a core value and knowledge that is co-owned and co-produced. This approach reframes “expertise” as something not so narrowly defined. Rather, credentialed experts contribute methodological or disciplinary knowledge, while experiential experts contribute embodied knowledge that cannot be replicated from someone external to the community and can ultimately work in tandem to shape inclusive knowledge. 

This project explicitly values both forms of expertise and pushes back against “deficit-based” thinking; the idea that formerly incarcerated people need some form of outside intervention, in a “saviorism” sort of way. Community members are co-researchers who have the capacity, autonomy, and agency to define the problems that matter to them and feel empowered in our partnership to have ownership of the research and the decisions that come from that research. This shift in framing is not just about language. There are implications for what research questions are asked, what methods are appropriate in finding the answers, how the data that forms those answers are evaluated, and then how findings are mobilized into practice. 

Research becomes more democratic and accountable by positioning experiential knowledge as a foundation because it aligns with real needs and increases the likelihood that solutions will actually work in practice. . We hope to actualize the core promise of community-led research: generating new knowledge by redistributing who has the “authority” to decide what knowledge is needed, why that knowledge is needed, and how that knowledge is acted upon. 

So, what does it look like to draw from both academic and lived perspectives? The following outlines perspectives from Mia, a credentialed researcher and Anthony, an experiential researcher. They explore some of the tensions that exist in our research questions, specifically when considering the subjectivity of “health” and “well-being.” Through the discourse, we consider the complications of evidence-based research and policymaking, in a conventional sense, and underscore the importance of valuing narratives and data to generate research, which frames our approach to “data analysis” and “findings.” 

Mia: When I think about the production of “research” in an academic context, I largely think about what was instilled in me throughout my graduate studies about objectivity. That the coveted “evidence-base” that we use to make decisions and shape policy has objective data that is derived from standardized and validated methodology in a way that confirms its credibility or reliability. And yet, our initial conversations with providers and formerly incarcerated people has highlighted that there is so much breadth and complexity that would be oversimplified by summarizing lived experiences into data points. Put simply: lived experience is “data” too, but it doesn’t fit neatly into charts and graphs. So, it gets you thinking, do numbers sometimes miss the point, particularly in this type of research? 

Anthony: While numbers can, in fact, miss the whole point of a community research project, that doesn’t necessarily have to be so. Our current work involved integrating the real opinions of research participants by using surveys, questionnaires, and interviews in order to fully incorporate their feelings on the matter at hand. We can translate those experiences into more quantitative data without marginalizing the very people we are trying to help.

Mia: This idea of turning narratives into quantitative data without marginalizing people makes me want to explicitly name the importance of “reflexivity.” Our perspectives as researchers, whether shaped by lived experience or academic research, will shape our knowledge production. There are assumptions, biases, and values that will influence the research process. It does not have to be a “bad thing,” but I see it as particularly important as we continue to incorporate different voices and perspectives into the outcomes of our research. 

Anthony: As for reflexivity, both qualitative and quantitative approaches will have this deficiency, just in different ways. Quantitative research is notorious for oversimplifying complex problems by reducing them to a few trite data points while qualitative approaches can stray too far off into their own narratives which may or may not be supported by evidence. All in all, I do believe that subjective data is essential for understanding the underlying structure of the problems we are trying to solve in community-based research, but that the numbers should be the primary emphasis in order to give our work greater credibility and therefore a greater impact.

Mia: I think it is important to think about why numbers automatically mean greater credibility. To me, it’s interesting to think about how to paint a comprehensive picture with only numbers in our research. Health, specifically emotional well-being, which is of course the focus of this research, is inherently subjective. While health is frequently medicalized and quantified in clinical contexts, even the concept of “pain,” for example, is not something precisely quantifiable. When I go to the doctor and they ask, how bad is your pain on a scale of 1-10, what if my definition of a “7” is different from theirs? Or what is the difference between a “6” and a “7?” I think we find that many facets of health follow that same level of subjectivity. Thus, the idea of optimal health, as an objective of healthcare, remains elusive and contested. It makes that subjective, qualitative data we are referring to seem quite important. 

Anthony: I think your point is well-taken in that subjective/qualitative data is equally important as hard-nosed numerical data when it comes to community-based research. The very fact that I, as a formerly incarcerated individual, am on this project speaks to how important lived experience is to eliciting insights into sociological problems that numbers have trouble illustrating. But, the public at large, as well as grant-makers and funders, is a skeptical audience with harsh critics with a worldview rooted in tough-on-crime narratives. In a time when there is public and political hostility towards diversity, equity, and inclusion, the interests of those communities we seek to serve must be appealing to a broadly diverse audience in order to gain the necessary credibility to continue our work. Specifically, policymakers overwhelmingly used striking numerical data to catch the public’s attention and then be leveraged politically to drive their decisions.

Mia: I couldn’t agree more that policymakers overwhelmingly use numerical data to drive their decisions, but that is also where I think we need to be more willing to challenge the status quo. My academic background is in Public Affairs and Public Health, so your comment immediately brought me back to my public policy training, specifically public policy analysis. In public policy, the best approach is to: Define the problem. Quantify it. Frame it around causes, severity, or affected populations. For example, we could define this issue by: insurance coverage gaps, rates of chronic illness, and mortality after release.. All of these examples could be used to shape a measurable policy issue that can then influence a clear path forward regarding policy goals and later evaluate outcomes. However, based on what we’ve started to hear from our focus groups, those data points are yes, measurable, but don’t quite get to the depth of the issue, wouldn’t you agree? 

Anthony: I agree that every data set needs a story because that is what gives numbers their power in the first place. That is why it needs to be the right blend of story to numbers so that people don’t feel that you’re selling them on a sob story. This is likely to make them feel manipulated and would be counterproductive to our goals. Our current work involves gathering stories through interviews and surveys and then translating those insights into day without losing what makes them meaningful.  

Mia: I think it’s important to name what numbers cannot always depict, and it starts to illustrate some of what we have been hearing from our focus group participants. For instance, one obvious part about healthcare access is both scheduling and attending a doctor’s appointment. Quantitative data could show how many appointments someone attends, but it couldn’t tell us about how stigma and discrimination impact that number. Formerly incarcerated people may feel judged and dismissed by their provider. We could ask: how would those experiences of treatment discourage healthcare access in the future? In this example, a raw number does not provide the whole picture. Another quantitative data point could show how many people do (or do not) have insurance because we know that will be a large influence on healthcare access. Of course, this is a very telling statistic due to the nature of our healthcare system, but it does not necessarily capture the experience of navigating bureaucratic barriers that someone would surely face in trying to enroll in insurance after their release. We could ask: what are the present institutional obstacles that would hinder formerly incarcerated people from seeking healthcare? I think we can take our analysis one step further by offering up answers to those questions. 

At its core, this work is about expanding what we consider valid knowledge. Numbers help us see patterns, but stories help us understand them. Academic research and the field of knowledge is better served by ensuring that the numbers are grounded authentically in context. This is vital if we want to create solutions that are not only effective on paper, but meaningful in practice. Stay tuned for part three in this series, which will reveal some of our research findings.

This article was written and contributed by Mia Williams, Anthony Argento, Dr. Karen Reece