Severe natural disasters carry great economic costs and threaten individuals' health and well-being in both the short and longer-term. Severe natural disasters during the past decade include unprecedented flooding, devastating wildfires, and catastrophic hurricanes. On September 14, 2018, Hurricane Florence made landfall off the coast of Wilmington, North Carolina, causing billions of dollars in damage to the state and surrounding region. Robeson County was one of the hardest hit counties in North Carolina, with catastrophic flooding causing substantial damage to residences, businesses, and infrastructure. The same county had experienced widespread damage following Hurricane Matthew in 2016 as well. Many in the hardest hit areas were still experiencing long-term impacts from Hurricane Matthew in 2018, including loss of employment and physical health problems related to exposure to mold and other contaminants. Research focused on the experiences of individuals in this county offers a unique opportunity to better understand how individuals cope with and recover from severe and ongoing stress in the post-disaster context. This project seeks to gain a richer understanding of how individuals manage post-disaster recovery and the extent to which individual and social factors predict both positive and negative outcomes. By integrating theories of post-disaster adjustment and using advanced data analytic techniques, the current study will lead to a better understanding of individual and community responses to extreme events and inform the development of future interventions.
Online surveys and daily "check in" assessments administered via smart phone technology will be used to study psychological adjustment, social support, community solidarity, and coping efforts among adult residents of Robeson County, North Carolina who were exposed to severe and repeated hurricane-related stress. The surveys and the daily "check-ins" will provide critical data on how people cope with stress over time, including the extent to which social and individual factors affect adjustment. By cultivating a very rich set of data on the same individuals over time, it will be possible to discern patterns of recovery, and understanding which we are currently lacking. Measuring outcomes on a daily basis will generate significant amounts of data and we will use advanced non-linear data analysis methods to identify unique changes or shifts in functioning predicted by guiding theoretical frameworks (self-regulation shift theory and social support deterioration deterrence theory). This project extends current knowledge related to post-disaster recovery by targeting key social and coping mechanisms of change and highlighting possible critical targets for interventions in the near and long-term post disaster context.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
