Evidence map›Paper›PMID 34682665›Full record

ArticleInternational journal of environmental research and public health2021

Social Network Correlates of Mental Health among Rural-to-Urban Migrants in China.

Wenqing Wang

Open access · goldAbstract read
In one paragraph

Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.8field-weighted citation impact, top 22% of its field
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

7 citing papers in PubMed, 10 citations in OpenAlex.

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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

1 author at 1 institution in 1 country.

Wenqing WangSchool of Humanities and Social Sciences, Beijing Institute of Technology, Beijing 100081, China.ORCID 0000-0002-4702-5091
Beijing Institute of Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Internal rural-to-urban migrants in China are facing a high risk of mental disorders. Previous research on mental health correlates and predictors among this population focused on individual-level characteristics, neglecting network-level indicators, and migrant-urbanite intergroup relationship. A cross-sectional survey was conducted in Beijing, China from December 2018 to January 2019. A convenience sample of 420 rural-to-urban migrants completed the Chinese 12-item General Health Questionnaire and reported their relationship with urbanites in the past six months. Multivariate linear regression models were used to test the association of the inter-hukou network with migrant mental health. Two indicators of the inter-hukou network were significantly associated with migrant mental health. Migrants were more mentally healthy if their proportion of weak ties in the inter-hukou network was no less than 50%. The more social support migrants received from the inter-hukou network, the better their mental health was. Meanwhile, there was a significant interaction effect between social support and sex, indicating that the same level of social support better protected the mental health of female migrants. Results suggest the importance of social network factors and migrant-urbanite ties for migrant mental health. Future efforts may need to mobilize and facilitate the inter-hukou network to improve migrant mental health.

Indexed as

Mental HealthTransients and MigrantsChinaCross-Sectional StudiesFemaleHumansRural PopulationSocial NetworkingUrban PopulationChinainter-hukou networkmental healthmigrant workerrural-to-urban migrantsocial networksocial supportweak ties

Identifiers

PMID34682665
PMCPMC8535324
OpenAlexW3205804469

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.