A temporal social resilience framework of communities to disasters in Australia

Abstract

Despite the current interest in and need for studies in the conceptualization and measurement of social resilience to hazards and disasters, there remains significant research gaps within this area. This study seeks to fill one such gap via the provision of an innovative unified framework of social resilience across three disaster phases (i.e., pre-disaster, response and recovery) using a quantitative research method. We utilized the survey results from the New South Wales State Emergency Service volunteers to validate a conceptualization framework that aimed to enhance social resilience across all disaster phases. This study had shown the positive correlation between identified indicators and social resilience but varying in impact strength depending on disaster phase.

The correlation between social resilience and flooding in low-income communities: a case of Mzuzu City, Malawi

“…The study method was quantitative. The quantitative design was adopted to measure the relationship between identified indicators (independent variables) of social resilience and social resilience (dependent variable) in the sample of participants (individual households) (Khalili et al , 2018). Purposive sampling technique was used to identify study participants.…”

“…Building and enhancing resilience to urban floods is one way to reduce socio-economic and physical losses and to improve quality of life (Khalili et al , 2018). The social components of resilience are increasingly emphasized as an approach favouring adaptation and transformation, in recognition that communities can learn from past environmental crises and modify practices and structures in preparation for future change (Clarke et al , 2017).…”

Toward a community resilience framework for disaster risk management. a case study: landslide Cisolok in Sukabumi 2018 and Sunda strait tsunami in Pandeglang 2018

“…Delayed recovery experience:  The more the physical items destroyed, the longer the recovery experience  Slower recovery experience if assistance received from private insurance companies  Larger size of households can delay the recovery effort Faster recovery experience:  Households recover early if having higher level of trust in the government  Households with denser personal networks experience quicker recovery  Households with higher geographic proximity of network partners experience early recovery  Households with assistance from neighbors experience faster recovery  Faster recovery experience if having previous disaster experience  Less time is required if longer time is spent in current home) [10]. Khalili et al (2018) state there are 14 social resilience indicators, i.e. :…”