Workload Analysis of Rapid Response Team Regional Disaster Management Agency at the Support Command Post of the COVID-19 Task Force Special Region of Yogyakarta Indonesia - Farihah, Tutik; Krisdiyanto, Didik; Murtono, Murtono; Khamidinal, Khamidinal (International Journal of Disaster Management, 2024)
This Study evaluated mental workload of Rapid Response Team ((RRT) Regional Disaster Management Agency in Special Region of Yogyakarta as funeral team along COVID-19 pandemic. Mental workload is formed due to differences between individual abilities and performance demands of a task within a certain time. NASA TLX is the most widely used mental workload measurement, capable of being used in several levels of workload and sensitive to low workloads. The Rapid Response Team is a team to ensure that the disaster management process carried out quickly, accurately, skilled personnel to back up the medical team who continue to work hard so that the handling of the pandemic virus is better, and the virus does not spread. In this study, the subject of research is the funeral team of Rapid Response Team ((RRT) Regional Disaster Management Agency in Special Region of Yogyakarta Indonesia. Sampling data was collected online and offline using the Goggle Form in the range March-April 2021. There are 28 team members of the RRT who filled out the questionnaire. Workload assessment using the NASA-TLX and OWL methods falls within the range of medium (45.58458; 0.610535), high (74.73789; 0.739889), and very high (87.7969; 0.879976), with an average workload value of high (75.9935; 0.748672). Based on statistical tests using paired t-tests and one-way ANOVA, both methods are declared to be equivalent. The dimension that predominantly contributes to workload according to the NASA TLX method is Effort, followed by Mental Demands. Meanwhile, the factor that predominantly forms the workload according to the OWL method is S2 (Environmental Workloads, sub-factors: improper temperature, chemical exposure), followed by S3 (Body Motion and Postural Workloads, sub-factors: stooping, standing). The research findings offer manual guidance for workload identification, particularly utilizing OWL, serving as the foundation for workload assessment for teams involved in COVID-19, particularly in Indonesia. Additionally, this study also demonstrates that the OWL method possesses the same level of reliability as the NASA-TLX method.