Evidence map›Paper›PMID 35342703›Full record

ArticleInternational journal of disaster risk reduction : IJDRR2022

What catalyzes the proactive recovery of peasants from the COVID-19 pandemic? A livelihood perspective in Ningqiang County, China.

Huizeng Zhao, Xuesong Guo, Ni Peng

Abstract read
In one paragraph

Article in International journal of disaster risk reduction : IJDRR, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Urban resilience to socioeconomic disruptions during the COVID-19 pandemic: Evidence from China.International journal of disaster risk reduction : IJDRR · 2023
    Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Huizeng ZhaoSchool of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, China.
Xuesong GuoSchool of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, China.
Ni PengSchool of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The livelihood recovery strategy is utilized for peasants during the COVID-19 pandemic. This strategy serves a salient role to help them recover from the relevant hazardous impacts. Disaster risk has been a major concern among hazards for the increasing likelihood of exposure and vulnerability, especially in the process of poverty alleviation in China. However, few studies have discussed the factors and mechanisms that influence peasants to adopt livelihood recovery strategies in the context of the COVID-19 pandemic in China. Based on a case study of Ningqiang County, China, this study explores the mechanisms that catalyze the proactive recovery of peasants from the COVID-19 pandemic from a livelihood perspective. Methodologically, the study proposes a framework that integrates the modified pressure-state-response (PSR) framework and the sustainable livelihoods approach (SLA), and it employs structure equation modeling (SEM) approach to examine how specific factors affect peasants to proactively adopt livelihood strategies to recover from the COVID-19 pandemic. The results indicate that the COVID-19 pressure significantly increases the risk perception of peasants and decreases their livelihood capital. Further, the decreased livelihood capital, the improvement of risk perception and supportive policy will promote peasants to adopt livelihood recovery strategies. Moreover, the results specify that risk perception and supportive policy mediate the relation between livelihood capital and recovery strategy. The findings will be beneficial for policymakers and researchers to understand the mechanisms that peasants adopt livelihood strategies to recover from disasters, and can serve as references for formulating disaster risk reduction and resilience policies.

Indexed as

COVID-19Disaster recoveryLivelihood strategyPressure-state-response frameworkRural China

Identifiers

PMID35342703
PMCPMC8940250

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.