Development of an Integrated Pathways Model of Factors Influencing the Progress of Recovery After a Disaster. (July 2020)

• Record Type: Journal Article
• Authors: Bhattarai, Sailesh; Maycock, Bruce; Alfonso, Helman; Reid, Alison
• Is Part Of: Asia-Pacific journal of public health. Volume 32:Number 5(2020)
• Page Start: 226
• Page End: 234
• Publication Date: 2020-07
• Subjects: disaster recovery -- integrated pathways model of disaster recovery -- factors influencing disaster recovery; Public health -- Pacific Area -- Periodicals; Public health -- Asia -- Periodicals; 362.1095
• DOI: 10.1177/1010539520935386
• Languages: English
• ISSNs: 1010-5395

Abstract

Disaster recovery is a complex and multidimensional process that is affected by the physical environment, social and economic conditions, and institutional strength and integrity. However, there is a lack of understanding as to why some communities recover quicker than others after experiencing the same disaster event. What are the critical factors needed for optimal disaster recovery and what factors predispose communities or individuals to poor disaster recovery? This article presents a literature-generated integrated pathways model of disaster recovery. A systematic search of the peer-reviewed literature identified 54 peer-reviewed publications that met our search criteria. The thematic content analysis of that literature revealed 14 factors that affected disaster recovery, which were clustered into 4 domains (social, physical/environmental, economic, and institutional/procedural). The integrated pathways model was developed to accommodate all of the domains and factors identified in the reviewed literature and the mediation and impact pathways that they influence. Using a combination of qualitative and quantitative data collected after the 2015 Nepal earthquake, the authors will examine and verify the interaction between domains and variables to identify those elements that are found in the most recovered and least recovered communities. The aim will be to modify and refine the model and enhance the understanding of the interaction between variables and to produce a data-driven model in order to better understand the factors that impede or enhance disaster recovery.