Objectives: Developing a disaster risk perception scale is a critical component of Disaster Risk Management (DRM), enabling the assessment and evaluation of the reactions, behaviors, and risk culture characteristics of individuals living under disaster risk. The objective of this study is to develop a disaster risk perception scale and to assess its effect on disaster preparedness.
Methods: A pilot study was conducted with 359 participants, followed by a main study involving 786 participants. All participants resided in Giresun and Elazig, Turkey, the regions recently affected by earthquakes, floods, and landslides.
Results: A reliable and valid disaster risk perception scale with 25 items and 5 dimensions (exposure/impact, probability, uncontrollable, worry/fear, and vulnerability) was developed. The disaster risk perception of the participants differed significantly according to their educational level, income level, city of residence, and disaster education. As per the multiple regression analysis, the exposure/impact and worry/fear variables had positive and significant effects on disaster preparedness.
Conclusions: For future studies, it is recommended to implement the disaster risk perception scale across diverse disaster types to assess and evaluate the outcomes effectively.
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http://dx.doi.org/10.1017/dmp.2025.38 | DOI Listing |
F1000Res
March 2025
Center for Management and Geospatial Information Dissemination, Geospatial Information Agency, Cibinong, West Java, 16911, Indonesia.
Background: The danger of earthquakes poses a serious threat to people worldwide. One of the most significant challenges is preparing communities to cope effectively with this disaster. Therefore, understanding earthquake hazards is critically important for preparedness, mitigation, and an effective response to this threat.
View Article and Find Full Text PDFRisk Anal
March 2025
Department of Civil, Environmental and Geomatic Engineering, University College London, London, UK.
Devastating earthquakes can cause affected households to relocate. Postearthquake relocation disrupts impacted households' social ties and, in some instances, their access to affordable services. Simulation-based approaches that model postearthquake relocation decision-making can be valuable tools for supporting the development of related disaster risk reduction (DRR) policies.
View Article and Find Full Text PDFJ Hazard Mater
March 2025
State Key Laboratory of Hydraulics and Mountain River Engineering, College of Architecture and Environment, Sichuan University, Chengdu 610065, PR China; Yibin Institute of Industrial Technology, Sichuan University Yibin Park, Yibin 644000, PR China; Sichuan University-The Hong Kong Polytechnic University Institute for Disaster Management and Reconstruction, Sichuan University, Chengdu 610065, PR China. Electronic address:
Typical water treatment processes are essential for mitigating the risk of microplastic contamination in drinking water. The integration of experiments and machine learning offers a promising avenue to elucidate microplastic removal behavior, yet relevant studies are scarce. To address this gap, this study combined experimental and artificial neural network (ANN) modeling to explore the removal behavior and mechanisms of five neglected microplastics in typical coagulation-ultrafiltration processes.
View Article and Find Full Text PDFJ Perinat Neonatal Nurs
March 2025
Author Affiliations: Erzincan Binali Yıldırım University, Faculty of Health Sciences, Department of Nursing, Division of Pediatric Nursing, Erzincan, Turkey (Mrs Kasımoğlu and Erzurum Technical University, Faculty of Health Science, Department of Nursing, Erzurum, Turkey (Mrs Gürol).
Objective: This study aimed to explore the relationship between disaster anxiety and prenatal attachment in pregnant women.
Method: A descriptive, cross-sectional design was used with 443 pregnant women recruited between April and August 2023. Data were collected using the Demographic Data Form, Disaster Anxiety Scale, and Prenatal Attachment Inventory.
PLoS One
March 2025
Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada.
Background: Ontario, being one of Canada's largest provinces, has been central to the high incidence of human Mpox. Research is scarce on how socio-environmental factors influence Mpox incidences. This study seeks to explore potential geographical correlations and the relationship between indicators of social marginalization and Mpox incidence rate in Ontario.
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