Dahae Rim1, Hyunsook Shin2, Hyejin Jeon1, Jieun Kim1, Hyojin Chun1, Hee Oh1, Soonyoung Shon3, Kaka Shim4, Kyung Mi Kim5. 1. College of Nursing Science, Kyung Hee University, Seoul, Korea. 2. College of Nursing Science, Kyung Hee University, Seoul, Korea. hsshin@khu.ac.kr. 3. College of Nursing, Keimyung University, Daegu, Korea. 4. Department of Nursing, College of Convergence Technology, Sang Myung University, Cheonan, Korea. 5. Department of Nursing Science, College of Medicine, Chungbuk National University, Cheongju, Korea.
Abstract
PURPOSE: We aimed to identify collaborative disaster governance through the demand and supply analysis of resources recognized by nurses during the COVID-19 pandemic. METHODS: We used a descriptive study design with an online survey technique for data collection. The survey questions were developed based on focus group interviews with nurses responding to COVID-19 and expert validity testing. A 42-question online survey focusing on disaster governance was sent to nurses working in COVID-19 designated hospitals, public health offices, and schools. A total of 630 nurses participated in the survey. Demand and supply analysis was used to identify the specific components of disaster governance during a pandemic situation and analyze priority areas in disaster governance, as reported by nurses. RESULTS: Demand and supply analysis showed that supplies procurement, cooperation, education, and environment factors clustered in the high demand and supply quadrant while labor condition, advocacy, emotional support, and workload adjustment factors clustered in the high demand but low supply quadrant, indicating a strong need in those areas of disaster governance among nurses. The nurses practicing at the public health offices and schools showed major components of disaster governance plotted in the second quadrant, indicating weak collaborative disaster governance. CONCLUSION: These findings show that there is an unbalanced distribution among nurses, resulting in major challenges in collaborative disaster governance during COVID-19. In the future and current pandemic, collaborative disaster governance, through improved distribution, will be useful for helping nurses to access more required resources and achieve effective pandemic response.
PURPOSE: We aimed to identify collaborative disaster governance through the demand and supply analysis of resources recognized by nurses during the COVID-19 pandemic. METHODS: We used a descriptive study design with an online survey technique for data collection. The survey questions were developed based on focus group interviews with nurses responding to COVID-19 and expert validity testing. A 42-question online survey focusing on disaster governance was sent to nurses working in COVID-19 designated hospitals, public health offices, and schools. A total of 630 nurses participated in the survey. Demand and supply analysis was used to identify the specific components of disaster governance during a pandemic situation and analyze priority areas in disaster governance, as reported by nurses. RESULTS: Demand and supply analysis showed that supplies procurement, cooperation, education, and environment factors clustered in the high demand and supply quadrant while labor condition, advocacy, emotional support, and workload adjustment factors clustered in the high demand but low supply quadrant, indicating a strong need in those areas of disaster governance among nurses. The nurses practicing at the public health offices and schools showed major components of disaster governance plotted in the second quadrant, indicating weak collaborative disaster governance. CONCLUSION: These findings show that there is an unbalanced distribution among nurses, resulting in major challenges in collaborative disaster governance during COVID-19. In the future and current pandemic, collaborative disaster governance, through improved distribution, will be useful for helping nurses to access more required resources and achieve effective pandemic response.
Authors: Stanley K K Lam; Enid W Y Kwong; Maria S Y Hung; Samantha M C Pang; Vico C L Chiang Journal: J Clin Nurs Date: 2018-01-11 Impact factor: 3.036
Authors: Radhika Chigurupati; Neeraj Panchal; Andrew M Henry; Hussam Batal; Amit Sethi; Richard D'innocenzo; Pushkar Mehra; Deepak G Krishnan; Steven M Roser Journal: J Oral Maxillofac Surg Date: 2020-05-24 Impact factor: 1.895
Authors: Regina L T Lee; Sancia West; Anson C Y Tang; Ho Yu Cheng; Connie Y Y Chong; Wai Tong Chien; Sally W C Chan Journal: Nurs Outlook Date: 2020-12-09 Impact factor: 3.250
Authors: Allison A Norful; Adam Rosenfeld; Krista Schroeder; Jasmine L Travers; Sainfer Aliyu Journal: Gen Hosp Psychiatry Date: 2021-01-10 Impact factor: 3.238