Technical capacity building is essential to foster innovation, enhance institutional systems, and integrate new capabilities for organizational and community needs. In order to center health equity and well-being in the analysis of health data, it is essential to develop technical infrastructures that are themselves more equitable and accessible to communities. Working with place data, or data that includes the additional dimension of location in its data structure, is crucial to the development of asset maps, visualization of health outcome patterns across neighborhoods, and exploration of the social determinants of health. While the basics of working with place data can be made accessible with a variety of free and/or open tools, pathways to working with such resources often remain difficult to access outside of niche and sometimes costly private or academic groups. Enabling the agency of communities (that disproportionately experience health inequity) in the processes of leveraging place data and mapping is essential to advance public health data in the 21st century. To address these challenges and advance technical capacity building for equity in community health, we developed, disseminated, and evaluated the SDOH & Place Community Toolkit. This toolkit aims to empower communities, public health professionals, and organizations by providing accessible resources for working with place-based data to identify spatial patterns, analyze health outcomes, and address geographic inequities. By integrating concepts such as Human-Centered Design (HCD), participatory mapping, and equity-centered frameworks, the toolkit bridges the gap between technical GIS applications and community-driven problem-solving. 

We examine initial local business activity responses to the Inflation Reduction Act (IRA). The IRA provides major supply-side investment incentives for clean energy, with estimates of clean energy investments already totaling over $280 billion. One focus of the IRA is to increase investment in disadvantaged communities by providing higher tax credits for investments in these communities. Using a unique dataset that captures firm-level clean energy investment announcements that are eligible for IRA tax incentives, we first identify county-level factors associated with the location of private firm clean energy investments post-IRA. We find counties with greater existing economic activity and higher unemployment rates are more likely to be selected for clean energy investments. Second, we investigate the extent to which firm-level clean energy investments are associated with county-level business activities. Our lower bound estimates indicate clean energy investments are associated with a 0.4 to 1.3 percent increase in county-level business establishments, suggesting IRA-related investments generate positive economic benefits. We also find some evidence of an increase in business activities in disadvantaged communities. Our findings contribute to the ongoing policy debate by quantifying the IRA’s economic impact on local economies. 

Despite growing emphasis on holistic wellness and flourishing, scholarship on mental health remains disproportionately focused on pathology rather than positive psychological outcomes, particularly among marginalized populations. This gap is especially relevant for African Americans, who face unique challenges due to racial oppression, trauma, and health inequities that may impact their pursuit of happiness and life satisfaction. However, despite these barriers, African Americans often demonstrate remarkable resilience across a number of psychological outcomes, a phenomenon scholars term the ‘Black-White mental health paradox’, which raises important questions about the specific factors that contribute to African Americans’ resilience and well-being. Using data from the National Survey of American Life,  we employed a random forest algorithm to examine how social, cultural, and contextual factors (e.g., racial identity, interpersonal relationships, religiosity, neighborhood context, etc.) predict positive well-being for African Americans (N=3,507). Random forest models explained 41% of the variance in happiness and life satisfaction, with self-rated health and financial security emerging as the most influential predictor domains. Perceived life positioning—where African Americans saw themselves on the “ladder of life”—was the most important individual predictor of well-being, with individuals who rated themselves closer to actualizing their best possible life reporting higher levels of happiness and satisfaction. Collectively, findings underscore the role that structural and socioeconomic factors play in promoting—or constraining—subjective well-being for African Americans and offer new insights for developing targeted interventions to enhance mental health and resilience in Black communities. This study also demonstrates how machine learning techniques can be used to advance theoretical understanding of the intersections between health, culture, and context and inform evidenced-based interventions to foster ‘Black Boy Joy’ and ‘Black Girl Magic’ in practice. 

The study of attitudes (evaluations of social entities along a positive–negative continuum) has been at the center of social psychological research since at least the 1930s. When and how attitudes change informs both accounts about fundamental processes of social learning and memory and translational endeavors of intervening on pernicious social group biases. Attitude change has traditionally been studied with a focus on short-term change, at the level of individuals, and using the experimental method. However, largescale continuous data collection via the Project Implicit educational website (http://implicit.harvard.edu/) now makes it possible to investigate how social group attitudes have changed in the long term, at the level of societies, and in response to cultural inputs. In this talk, I will use data from 1.2 million+ participants across 33 countries, drawn from the openly available Project Implicit International Dataset (Charlesworth, Navon, et al., 2023), to ask whether and how explicit (self-reported) and implicit (automatically revealed) attitudes toward five social group targets (age, body weight, sexuality, skin tone, and race) changed around the world between 2009 and 2019. In addition to presenting overall trends, I will decompose results into age, period, and cohort effects; review cross-country variability; compare findings to the same time window in the United States; discuss key demographic comparisons; and identify key ecological correlates of change. I will conclude by highlighting implications of the observed patterns of stability and change for both theoretical accounts of social cognitive processes and the possibility of positive societal change.