Jessica Liddell1, Regardt J Ferreira2. 1. 1City,Culture and Community PhD Program,School of Social Work,Tulane University,New Orleans,LA. 2. 2School of Social Work,Tulane University,New Orleans,LA.
Abstract
OBJECTIVE: Literature explores which factors most impact resilience and how these factors impact an individual and communities' ability to cope with disaster. Less research has focused on how age impacts resilience. This research adapts several previous conceptual models used to investigate resilience. To investigate the unique vulnerabilities faced by older individuals in post-disaster settings, this analysis was undertaken to investigate predictors of individual resilience. METHODS: Data for the study were derived from the Centers for Disease Control and Prevention (CDC) Gulf States Population Survey (GSPS). The final sample included 5,713 adult residents from 4 gulf-coast states. Multiple linear regression was used for the analysis. RESULTS: All models (demographic, health, social, and combined) acted as significant predictors of individual resilience. Health and social resilience models accounted for more of the variance in resilience scores. In all models, age was negatively associated with resilience scores. Being female was protective across all models. The results of the model testing indicate inequitable disaster mitigation, with social and health indicators explaining the most variance in the resilience levels. CONCLUSION: This research provides practitioners with the knowledge they need to focus their interventions on the areas where it is most needed to empower resilient individuals. (Disaster Med Public Health Preparedness. 2019;13:256-264).
OBJECTIVE: Literature explores which factors most impact resilience and how these factors impact an individual and communities' ability to cope with disaster. Less research has focused on how age impacts resilience. This research adapts several previous conceptual models used to investigate resilience. To investigate the unique vulnerabilities faced by older individuals in post-disaster settings, this analysis was undertaken to investigate predictors of individual resilience. METHODS: Data for the study were derived from the Centers for Disease Control and Prevention (CDC) Gulf States Population Survey (GSPS). The final sample included 5,713 adult residents from 4 gulf-coast states. Multiple linear regression was used for the analysis. RESULTS: All models (demographic, health, social, and combined) acted as significant predictors of individual resilience. Health and social resilience models accounted for more of the variance in resilience scores. In all models, age was negatively associated with resilience scores. Being female was protective across all models. The results of the model testing indicate inequitable disaster mitigation, with social and health indicators explaining the most variance in the resilience levels. CONCLUSION: This research provides practitioners with the knowledge they need to focus their interventions on the areas where it is most needed to empower resilient individuals. (Disaster Med Public Health Preparedness. 2019;13:256-264).