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Community Responses to Mass Casualty Events: A Mixed Method Approach

This dissertation explores how traits of a mass casualty event and community institutionalization affect how a community demonstrates solidarity after a mass casualty event. A systematic examination of mass casualty events along these lines has not been conducted before. Theoretically, individual helping behaviors like altruism help explain individual involvement in demonstrations of solidarity while solidarity and resilience help in explaining group behaviors. A typology is proposed that breaks up mass casualty events into four different types: terrorism, criminal, weather and accidents. These types of events make up the majority of non-war mass casualty events. Experimentally a sample of students is used to assess how individuals are likely to respond to mass casualty events by gauging how they would respond using five different types of demonstrations of solidarity. Findings suggest that victim type positively influences demonstrations of solidarity while casualty number and event type are only selectively influential. Two cases (Orlando, FL 2016 and San Bernardino, CA 2015) are used to test three hypotheses that are related to how a community demonstrates solidarity after a mass casualty event. Results indicate that victim type positively influences demonstrations of solidarity, particularly through the specific institutions within vulnerable communities that increased access to demonstrations. Additionally, increased institutionalization within the victim community also positively influences demonstrations of solidarity. Furthermore, results suggest that event specific traits do influence demonstrations of solidarity under certain circumstances. However, more empirical research is needed to examine how individuals respond and the exact processes available to communities that would aid in their recovery from such an event.

Identiferoai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd-7765
Date01 January 2019
CreatorsPayne, Jeffrey
PublisherSTARS
Source SetsUniversity of Central Florida
LanguageEnglish
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceElectronic Theses and Dissertations

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