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

Abstract

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.

Surrogate Measures to Assess Mobility of People as a Resilience Indicator in Disaster Management: An Exploratory Study in Southeastern Sri Lanka

“…Saja et al (2019) discussed various approaches used to develop resilience frameworks and methods applied in resilience measurement. When the existing methods of measuring resilience are categorized as ''topdown'' and ''bottom-up'' methods, for example, the key difference between these two methods is the purpose and application (Schoch-Spana et al 2019). Top-down methods largely depend on quantitative indicators, which are helpful for resilience investment decision making through comparisons between multiple communities, whereas bottomup methods are driven by a local perspective on resilience and the integration of experiential knowledge that are helpful for the direct local application of results (Cutter and Derakhshan 2019;Schoch-Spana et al 2019).…”

“…When the existing methods of measuring resilience are categorized as ''topdown'' and ''bottom-up'' methods, for example, the key difference between these two methods is the purpose and application (Schoch-Spana et al 2019). Top-down methods largely depend on quantitative indicators, which are helpful for resilience investment decision making through comparisons between multiple communities, whereas bottomup methods are driven by a local perspective on resilience and the integration of experiential knowledge that are helpful for the direct local application of results (Cutter and Derakhshan 2019;Schoch-Spana et al 2019). Doorn (2017) highlighted that the use of non-aggregated indicators can be more useful for contributing to the detailed analysis of resilience than aggregated indices-the latter are mainly helpful in the evaluation of intervention effectiveness and in drawing attention to an issue.…”

Improving Community Resilience and Emergency Plans by Mapping Risk and Preparedness at the Neighborhood Scale

“…Following the RQ rationale and inspired by new tools being developed [53,41], we built an online Personal Readiness Evaluation tool (PRE; scale from 0 to 10). The PRE score is calculated based on answers to 13 questions covering the following aspects: infrastructure (instrumental preparedness, four questions accounting for 35% of the overall score), action plan for during and right after an earthquake (2 questions, 15% of score), Physical disability and emotional/experience factors (2 questions, 15% of score), community involvement and support (2 questions, 15% of score), and awareness and risk perception (2 questions, 20% of score).…”

Resilience Assessment of Complex Urban Public Spaces

“…Zhang et al considered 56 prefecture-level cities in China to explore the spatial distribution of urban resilience and its influencing factors of uncertainty disturbances [15]. Schoch-Spana et al produced a self-assessment toolkit-the Composite of Post-Event Well-being rubric-to predict post-disaster community function and resilience [16]. Tiernan et al identified three important emerging themes: the socialization of responsibility for resilience, concentration on utilizing public private partnership to continue risk management, and the exploration of adaptive resilience [17].…”

“…The super-matrix W ij of each cluster can be obtained by transposing the relevant clusters of the normalized total-influence matrix T α c . The unweighted super-matrix W is shown as Equation (16). For example, the matrix W 11 is a collection of factor influence values connected with factors in cluster D 1 .…”