The COPEWELL Rubric: A Self-Assessment Toolkit to Strengthen Community Resilience to Disasters.

Measurement is a community endeavor that can enhance the ability to anticipate, withstand, and recover from a disaster, as well as foster learning and adaptation. This project's purpose was to develop a self-assessment toolkit-manifesting a bottom-up, participatory approach-that enables people to envision community resilience as a concrete, desirable, and obtainable goal; organize a cross-sector effort to evaluate and enhance factors that influence resilience; and spur adoption of interventions that, in a disaster, would lessen impacts, preserve community functioning, and prompt a more rapid recovery. In 2016-2018, we engaged in a process of literature review, instrument development, stakeholder engagement, and local field-testing, to produce a self-assessment toolkit (or "rubric") built on the Composite of Post-Event Well-being (COPEWELL) model that predicts post-disaster community functioning and resilience. Co-developing the rubric with community-based users, we generated self-assessment instruments and process guides that localities can more readily absorb and adapt. Applied in three field tests, the Social Capital and Cohesion materials equip users to assess this domain at different geo-scales. Chronicling the rubric's implementation, this account sheds further light on tensions between community resilience assessment research and practice, and potential reasons why few of the many current measurement systems have been applied.

COPEWELL: A Conceptual Framework and System Dynamics Model for Predicting Community Functioning and Resilience After Disasters.

OBJECTIVE: Policy-makers and practitioners have a need to assess community resilience in disasters. Prior efforts conflated resilience with community functioning, combined resistance and recovery (the components of resilience), and relied on a static model for what is inherently a dynamic process. We sought to develop linked conceptual and computational models of community functioning and resilience after a disaster. METHODS: We developed a system dynamics computational model that predicts community functioning after a disaster. The computational model outputted the time course of community functioning before, during, and after a disaster, which was used to calculate resistance, recovery, and resilience for all US counties. RESULTS: The conceptual model explicitly separated resilience from community functioning and identified all key components for each, which were translated into a system dynamics computational model with connections and feedbacks. The components were represented by publicly available measures at the county level. Baseline community functioning, resistance, recovery, and resilience evidenced a range of values and geographic clustering, consistent with hypotheses based on the disaster literature. CONCLUSIONS: The work is transparent, motivates ongoing refinements, and identifies areas for improved measurements. After validation, such a model can be used to identify effective investments to enhance community resilience. (Disaster Med Public Health Preparedness. 2018;12:127-137).