Mental Health Policymaking That Integrates Research Evidence and Community Engagement

(Illustration by dickcraft)

Local and personal factors, such as neighborhood, race, gender, and age, significantly influence our mental health status. Between 2000 and 2020, for example, rural communities experienced a 46 percent rise in suicide rates compared to a smaller, though still concerning, 27 percent rise in metro areas. And it is well known that communities of color experience less access to mental health services than white communities despite similar levels of need.

Current mental health policymaking tends to be insufficiently sensitive to these differences. Policy bodies like the National Governor’s Association are calling for more tailored mental health planning. However, state, county, and city governments are inconsistently skilled at developing local solutions. A recent study by the university policy research center I direct, CoLab for Community and Behavioral Health Policy, confirmed this capacity gap among mental health “policy intermediaries”—nongovernmental organizations that work closely with government to inform policy development. Among the more than 80 organizations in the United States, Canada, Britain, and Australia we surveyed, working with local communities was one of the least endorsed activities. Only 10 percent of organizations reported community engagement as a core activity of their policy support strategies. Information management capacities, such as reviewing and synthesizing the relevant research evidence, were much more common.

To be sure, the use of research evidence in policymaking is also valuable. Taxpayers should expect governments to steward resources responsibly and in a way that maximizes benefit to all citizens. However, in social policy, academics and policy makers can use the rhetoric of evidence-based policy in policy deliberations to dismiss rather than resolve the complex ways that community values and research evidence interact. After nearly 20 years of experience developing and studying mental health policy, I see the need for governments to use mental health and social science differently. Adequately addressing the local mental health needs of states, counties, and cities requires that governments and their partner organizations develop three interrelated competencies: 1) the ability to integrate research evidence with local information, 2) the ability to design solutions from the ground up to respond to local values, and 3) the ability to maintain trust and relationships within a political network over time.

Integrating Information

Developing the capacity to integrate knowledge from research with knowledge of local needs, preferences, and system capacities requires that policy makers value multiple types of information equally. For the last 20 years or so, philanthropic and academic advocates for better social policymaking have emphasized the use of evidence-based practices. In this narrative, community engagement is not maligned, but it is framed as a “nice if you can do it” activity. For example, the World Health Organization’s guide for evidence-informed decision-making outlines hierarchies of evidence beginning with systematic reviews and ending with “colloquial” evidence such as citizen panels. However, underestimating the importance of local context and values when applying evidence can result in inelegant, blunt applications of the research. A 2013 study of 10 structured programs implemented by the State of Pennsylvania to prevent youth substance misuse and crime found that 44 percent of workers delivering these programs made on-the-fly adaptations because they lacked the time, resources, or interest in delivering the program exactly as written. Policies that lack input from citizens and service providers are likely to fail in implementation.

When looking for mental health solutions, policy makers are often reliant on academics and treatment developers who, understandably, are looking for markets for their tested programs. It is relatively rare to find academics who work closely with governments to apply insights from research evidence in a way that responds to local needs, but some notable examples exist. Kimberly Hoagwood, MD is a nationally renowned child mental health services researcher at New York University (NYU) with more than 300 peer review publications, and continuous funding from the National Institutes of Mental Health. By preference, although unusual for someone with her credentials, she works in close collaboration with New York State, parent and youth consumers of mental health services, and the state’s technical assistance center at NYU’s McSilver Institute. In this role, she is deeply involved in assisting mental health treatment agencies across the state, and her research lab acts as a de facto design space, examining real-world relevant programs and training infrastructure at scale.

What makes the work in Hoagwood’s lab (and at the McSilver Institute in general) different from many other academic centers is the integration of multiple perspectives in the program development stage rather than at the end of the translational cycle. This is fostered by a long-time collaboration between a New York state-funded mental health Community Technical Assistance Center and the researchers working with Hoagwood’s lab. The cross-fertilization of ideas increases the development of solutions directly applicable to New York State’s family and service sector needs. This flips the typical academic approach to innovation, which prioritizes rigorous trials of a highly controlled protocols before the dissemination of knowledge. In contrast, the approach at Hoagwood’s center views knowledge as ever-changing and iterative, which allows the team to craft solutions more responsive to the policy environment. In this way, diverse perspectives enrich the research, and the impact is more immediate and more widespread than typical academic research translation.

This is happening at a time when thought leaders and funders are slowly moving away from defining evidence-based policymaking as simply adopting programs that academic centers test and develop. For example, The Pew Charitable Trusts, an influential leader in the evidence-based policy movement, increasingly focuses on asking how policy makers should incorporate evidence rather than assuming its application can take only one form. This does not imply that policy makers should avoid adopting evidence-based programs. Rather, policy makers and their consultants need to add more skills and capacities to their decision-making toolbox, including creative methods to integrate knowledge. One of the reasons researchers in Hoagwood’s lab feel confident using a collaborative approach is because they are the world’s foremost subject matter experts in their area and can trust the best knowledge about children’s mental health will be included in their efforts. But we cannot restrict more creative and effective social innovation to just a few centers with highly specialized experts. If governments and social innovators want to increase creative and robustly informed policymaking, decision-makers will need tools that build this innovation confidence muscle internally. Design thinking approaches can help.

Designing Solutions From the Ground Up

The most frequent hurdles my team encounters when launching into a new policy design area are preconceived ideas among our partners about what is possible or negotiable. We all come to problem-solving efforts with personal and professional histories and interests. These histories enrich but can also get in the way of serving the public problem itself. Our team uses methods from the field of participatory design to help teams navigate their way through dismantling and then rebuilding a set of possible solutions. Participatory design normalizes and even valorizes stepping into ambiguity, uncertainty, and complexity. This ability to tolerate complexity and uncertainty may be the most critical aspect of better social policy design.

Equipping policymaking collaborators with concrete tools to move through a design process helps manage the anxiety and skepticism that often emerges for participants in the early, ambiguous phases of development. An example comes from the Health Design Studio at OCAD University, the largest art, design, and media university in Canada. The studio is working with Public Health Ontario to codesign an opioid overdose strategic plan with broad and deep input from community members. The OCAD design team uses specific strategies and methods to elicit feedback from underheard voices, organize that information, and reach sufficient agreement among community participants to formally advance a plan.

Design methods give structure and shape to these deliberations. For example, the OCAD used idea matrices: five-by-five tables listing important steps in an individual’s opioid misuse journey in columns and “key enablers” in rows. Participants then filled in their ideas for how the public health department could improve prevention and response at different points in the journey. Organizing information in this way can help surface possible disagreements or agreements more quickly than listening session-style workshops can, as well as spark more detailed brainstorming and innovation. Research evidence comes into these types of engagements as one stream of information among many. Rather than setting boundaries around innovation, it is an information source also subject to deliberation.

My center is similarly trying and testing models of information integration in mental health policy development. We are encountering surprising findings that challenge some of the existing assumptions of academia about how to translate research evidence in policy design. In a study examining the process of developing a jail-based and reentry program for opioid overdose prevention, for example, we found that the multiple sectors involved in planning valued information they learned from each other more than research evidence presented during the effort. One participant was most interested in learning about existing community services that could help individuals leaving the jail. Absent any requirement that the team use the research evidence, the policy design team produced a reentry program that reflected the evidence base while including innovative components that enhanced fit for their community. The program was successfully implemented in six months and has been sustained now for over three years.

While not all efforts will see this type of success and certainly not all policy design efforts will serendipitously land on evidence-support solutions, these efforts suggest that governments can adopt more human-centered rather than research-centered policymaking processes without losses in quality.

Trust in a Policy Network

Any individual who works in a policy arena understands that policymaking is highly relational. A policy maker (or intermediary) has to mobilize a policy network in addition to identifying a good policy solution. Even the best policy ideas have to achieve credibility within a broad policy network before policy makers can change a law or launch a new initiative. Policy briefs, analyses, strategic plans, and recommendations that do not simultaneously engage agreement within a policy network are unlikely to achieve meaningful impact.

In cases where researchers or policy intermediaries do not have credibility in the existing policy network (including service, advocacy, community, government representatives), it is a good idea to collaborate with one or more trusted organizers or conveners. For example, the ASPEN project at Rutgers University is a collaboration between communication and mental health scientists and the National Alliance on Mental Illness New Jersey (NAMI-NJ) to address youth depression through knowledge sharing and policy advocacy. NAMI-NJ is trusted in the policy arena and more equipped to influence policy ideas among policy makers and partners than the scientists. Paired with robust policy design processes, this kind of collaboration produces a powerful combination of strong policy ideas and strong network activation. Governments that pursue policy design should similarly consider collaborations with community and advocacy organizations, which offer an accountability check on proposed ideas and beneficial cross-fertilization of expertise.

Building the capacity of government and policy intermediaries to engage with multiple information sources will never completely resolve complexity, but this is the point. Reframing applied social science’s core purpose as managing complexity rather than reducing complexity will yield large returns on investment. To do this, governments, particularly local governments, must adopt new ways of thinking about expertise and evidence; fortunately, the tools and strategies for better mental health policymaking are available. In embracing a design approach, we can move toward greater balance in values and knowledge, more democratic process, and more responsive services.

Read more stories by Sarah Cusworth Walker.