Affiliation:
1. Division of Engineering Technology, Oklahoma State University, 570 Engineering North, Stillwater, OK 74078, USA
2. Incheon Metropolitan City Fire Department, Incheon Metropolitan City, South Korea
Abstract
COVID-19 acted as a "Disease X", challenging governments around the world in early 2020. Rightly or wrongly, a local government in China handled the pandemic response with great uncertainty, triggering immediate consequences. While the world has entered the post-COVID era, it is still unknown how residents perceived the governmental responses in aligning policies to the public needs under such a great level of uncertainty. This study reevaluated the public's perception of the earliest Chinese government response to the COVID-19 pandemic using the interpretivist paradigm and content analysis. The qualitative and inductive research design used a snowball sampling method and collected 111 semi-structured questionnaire responses in February 2020. A framework of three themes, eight categories, and forty-five codes was developed from the findings, covering knowledge of the virus, information sources, information reliability, quarantine communication, quarantine support, needs during quarantine, improvement recommendations, and hopes and questions. Built from the lived experiences of the Chinese public during the first COVID-19 lockdowns, the posited framework encapsulated the public's perception of the government's response to this pandemic. To conclude, we drew three policy recommendations based on the three key themes to improve preparedness for future health crises and pandemics—disaster communication, integrated pandemic preparedness system, and hope and uncertainty. In particular, (1) a government-developed public information system can promote two-way communication and reduce information asymmetry; (2) an integrated system can better coordinate resource allocation; (3) the warning messages can correspond to the hope and positive feelings in the public norms.
Publisher
American Institute of Mathematical Sciences (AIMS)
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