Evidence map›Paper›PMID 35627728›Full record

ArticleInternational journal of environmental research and public health2022

Social Stability Risk Assessment of Disaster-Preventive Migration in Ethnic Minority Areas of Southwest China.

Linyi Zhou, Demi Zhu, Wei Shen

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. 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.

Linyi ZhouSchool of International and Public Affairs, Shanghai Jiao Tong University, Shanghai 200030, China.
Demi ZhuSchool of International and Public Affairs, Shanghai Jiao Tong University, Shanghai 200030, China.
Wei ShenSchool of Economics and Management, Tongji University, Shanghai 200092, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Disaster-preventive migration (DPM) is an important method for disaster risk management, but migration itself entails a potential social stability risk. This study took County D in Yunnan Province, one of the counties most severely threatened by geological disasters in China, as an example to construct an indicator system of social stability risk factors for disaster-preventive migration based on a literature survey and in-depth interviews. The system consists of 5 first-level risk factors and 14 s-level risk factors. The social stability risk of DPM in County D was assessed using a fuzzy comprehensive evaluation method based on experts’ weights. The results showed that the overall social stability risk level of disaster-preventive migration in County D is ‘high’. In terms of importance, the five first-level risk factors were ranked as follows: public opinion risk > compensation risk > livelihood recovery risk > cultural risk > geological disaster risk. Among the risk factors, the level of public opinion risk and compensation risk appeared to be high, whereas that of livelihood recovery risk, cultural risk and geological disaster risk resulted to be medium. To our knowledge, this paper is the first research to evaluate the social stability risk of DPM; it not only enriches the theories of social stability risk assessment, but also has important guiding significance for people relocation and resettlement in Chinese ethnic minority areas.

Indexed as

DisastersEthnic and Racial MinoritiesChinaEthnicityHumansMinority GroupsRisk AssessmentChinadisaster-preventive migration (DPM)ethnic minority areafuzzy comprehensive evaluation (FCE)social stability risk

Identifiers

PMID35627728
PMCPMC9141950

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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.