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1.
Chinese Public Administration Review ; 12(1):72-81, 2021.
Article in English | ProQuest Central | ID: covidwho-2305860

ABSTRACT

To cope with the COVID-19 pandemic, the Chinese government initiated a medical resource allocation and assistance mechanism that was characterized as a large-scale and regional mutual approach. Specifically, thirty provinces delivered medical resources (e.g., medical staff, medical supplies, and living materials) to "1+16” cities severely affected by the epidemic within a small amount of time, which solved the dilemma of medical collapse and governance "downtime” in epidemic areas, thereby changing the prevalence curve of the pandemic in China. "Campaign-style” targeted assistance can be interpreted based on the Chinese dual party-government model as well as the governance model of vertical accountability and horizontal competition, drawing from previous experience of normalized "designated assistance.” Consequently, paired assistance contributes to intergovernmental situations of decreasing divisibility and increasing cooperation. This study has the potential to bring insights to other countries around the world that are fighting the COVID-19 pandemic.

2.
Front Public Health ; 10: 885852, 2022.
Article in English | MEDLINE | ID: covidwho-1903228

ABSTRACT

To control the coronavirus pandemic (COVID-19), China implemented the Paired Assistance Policy (PAP). Local responders in 16 cities in Hubei Province were paired with expert teams from 19 provinces and municipalities. Fully supported by the country's top-down political system, PAP played a significant role in alleviating the COVID-19 pandemic in Hubei Province and China as a whole. In this study, we examined PAP using a two-way fixed effects model with the cumulative number of medical support personnel and cumulative duration as measurements. The results show personnel and material support played an active role in the nation's response to the COVID-19 public health crisis.


Subject(s)
COVID-19 , COVID-19/epidemiology , China/epidemiology , Humans , Pandemics , Policy , SARS-CoV-2
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