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The COVID-19 pandemic has exacerbated health disparities and rendered them acutely more visible. Special and underrepresented populations need to be fully integrated into the translational research process from the very beginning and all the way through. This article presents findings and rapid analysis mini-case studies from the Environmental Scan (E-Scan) of adaptive capacity and preparedness of Clinical and Translational Science Award hubs, specific to the goal of integrating special and vulnerable populations in different institutional research settings. In our discussion of the findings and case studies, we flexibly apply local adaptive capacity framework concepts and characteristics, and, whenever possible, we present ideas on how to enhance capacity in those areas, based on the challenges and practices identified through the E-Scan. Although the past year has recorded incredible achievements in vaccine development, clinical trials, diagnostics, and overall biomedical research, these successes continue to be hampered by our inability to turn them into achievements equally available and accessible to all populations.

This article proposes a more nuanced method to assess the accuracy of preelection polls in competitive multiparty elections. Relying on data from the 2006 and 2012 presidential campaigns in Mexico, we illustrate some shortcomings of commonly used statistics to assess survey bias when applied to multiparty elections. We propose the use of a Kalman filter-based method that uses all available information throughout an electoral campaign to determine the systematic error in the estimates produced for each candidate by all polling firms. We show that clearly distinguishing between sampling and systematic biases is a requirement for a robust evaluation of polling firm performance, and that house effects need not be unidirectional within a firm's estimates or across firms.

This paper is part of the Environmental Scan of Adaptive Capacity and Preparedness of Clinical and Translational Science Award (CTSA) hubs, illuminating challenges, practices, and lessons learned related to CTSA hubs’ efforts of engaging community partners to reduce the spread of the virus, address barriers to COVID-19 testing, identify treatments to improve health outcomes, and advance community participation in research. CTSA researchers, staff, and community partners collaborated to develop evidence-based, inclusive, accessible, and culturally appropriate strategies and resources helping community members stay healthy, informed, and connected during the pandemic. CTSA institutions have used various mechanisms to advance co-learning and co-sharing of knowledge, resources, tools, and experiences between academic professionals, patients, community partners, and other stakeholders. Forward-looking and adaptive decision-making structures are those that prioritize sustained relationships, mutual trust and commitment, ongoing communication, proactive identification of community concerns and needs, shared goals and decision making, as well as ample appreciation of community members and their contributions to translational research. There is a strong need for further community-engaged research and workforce training on how to build our collective and individual adaptive capacity to sustain and improve processes and outcomes of engagement with and by communities—in all aspects of translational science.