In the 21st century, the surge in natural and human-induced disasters necessitates robust disaster managementframeworks. This research addresses a critical gap, exploring dynamics in the successful implementation andp...In the 21st century, the surge in natural and human-induced disasters necessitates robust disaster managementframeworks. This research addresses a critical gap, exploring dynamics in the successful implementation andperformance monitoring of disaster management. Focusing on eleven key elements like Vulnerability and RiskAssessment, Training, Disaster Preparedness, Communication, and Community Resilience, the study utilizesScopus Database for secondary data, employing Text Mining and MS-Excel for analysis and data management.IBM SPSS (26) and IBM AMOS (20) facilitate Exploratory Factor Analysis (EFA) and Structural Equation Modeling(SEM) for model evaluation.The research raises questions about crafting a comprehensive, adaptable model, understanding the interplaybetween vulnerability assessment, training, and disaster preparedness, and integrating effective communicationand collaboration. Findings offer actionable insights for policy, practice, and community resilience against disasters. By scrutinizing each factor's role and interactions, the research lays the groundwork for a flexible model.Ultimately, the study aspires to cultivate more resilient communities amid the escalating threats of an unpredictable world, fostering effective navigation and thriving.展开更多
This paper discusses the utilization of latent variable modeling related to occupational health and safety in the mining industry.Latent variable modeling,which is a statistical model that relates observable and laten...This paper discusses the utilization of latent variable modeling related to occupational health and safety in the mining industry.Latent variable modeling,which is a statistical model that relates observable and latent variables,could be used to facilitate researchers’understandings of the underlying constructs or hypothetical factors and their magnitude of effect that constitute a complex system.This enhanced understanding,in turn,can help emphasize the important factors to improve mine safety.The most commonly used techniques include the exploratory factor analysis(EFA),the confirmatory factor analysis(CFA)and the structural equation model with latent variables(SEM).A critical comparison of the three techniques regarding mine safety is provided.Possible applications of latent variable modeling in mining engineering are explored.In this scope,relevant research papers were reviewed.They suggest that the application of such methods could prove useful in mine accident and safety research.Application of latent variables analysis in cognitive work analysis was proposed to improve the understanding of human-work relationships in mining operations.展开更多
文摘In the 21st century, the surge in natural and human-induced disasters necessitates robust disaster managementframeworks. This research addresses a critical gap, exploring dynamics in the successful implementation andperformance monitoring of disaster management. Focusing on eleven key elements like Vulnerability and RiskAssessment, Training, Disaster Preparedness, Communication, and Community Resilience, the study utilizesScopus Database for secondary data, employing Text Mining and MS-Excel for analysis and data management.IBM SPSS (26) and IBM AMOS (20) facilitate Exploratory Factor Analysis (EFA) and Structural Equation Modeling(SEM) for model evaluation.The research raises questions about crafting a comprehensive, adaptable model, understanding the interplaybetween vulnerability assessment, training, and disaster preparedness, and integrating effective communicationand collaboration. Findings offer actionable insights for policy, practice, and community resilience against disasters. By scrutinizing each factor's role and interactions, the research lays the groundwork for a flexible model.Ultimately, the study aspires to cultivate more resilient communities amid the escalating threats of an unpredictable world, fostering effective navigation and thriving.
基金Natural Sciences and Engineering Research Council of Canada(NSERC)(ID:236482)for supporting this research
文摘This paper discusses the utilization of latent variable modeling related to occupational health and safety in the mining industry.Latent variable modeling,which is a statistical model that relates observable and latent variables,could be used to facilitate researchers’understandings of the underlying constructs or hypothetical factors and their magnitude of effect that constitute a complex system.This enhanced understanding,in turn,can help emphasize the important factors to improve mine safety.The most commonly used techniques include the exploratory factor analysis(EFA),the confirmatory factor analysis(CFA)and the structural equation model with latent variables(SEM).A critical comparison of the three techniques regarding mine safety is provided.Possible applications of latent variable modeling in mining engineering are explored.In this scope,relevant research papers were reviewed.They suggest that the application of such methods could prove useful in mine accident and safety research.Application of latent variables analysis in cognitive work analysis was proposed to improve the understanding of human-work relationships in mining operations.
