Evidence map›Paper›PMID 35095352›Full record

ArticleInternational journal of mental health and addiction2022

Network Analysis of Media Exposure and Psychological Outcomes During the Initial Outbreak of COVID-19 in China.

Shu Zhang, Tour Liu, Xiaorui Liu, Miao Chao

Abstract read
In one paragraph

Article in International journal of mental health and addiction, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
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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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

2 citing papers in PubMed.

  1. Article
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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

4 authors.

Shu ZhangShanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, School of Psychology and Cognitive Science, East China Normal University, Shanghai, 200062 China.
Tour LiuKey Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin, 300387 China.ORCID 0000-0002-1186-2357
Xiaorui LiuFaculty of Psychology, Tianjin Normal University, Tianjin, 300387 China.
Miao ChaoKey Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin, 300387 China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

During the initial outbreak of COVID-19 in China, people spent a lot of time viewing or listening to COVID-19-related media content. And according to the conservation of resources theory, COVID-19-related media exposure would be related to multiple psychological outcomes, and different contents would have different associations with different psychological outcomes. Therefore, the present study aimed to clarify these relationships and find out the most important psychological outcome correlated with COVID-19-related media exposure. An online survey was conducted to collect information about individuals' media use behaviors and mental health status. A total of 917 participants were included in further analyses. The results proved that different kinds of content had different relationships with different psychological outcomes. To be more specific, the content about people being heroic and speeches from experts and the authorities were related to increases of positive affect, while reports from hospitals were correlated with increases of death anxiety. Moreover, positive affect was central nodes of the network. The current findings indicated viewing objective and positive content was associated with positive psychological outcomes while viewing negative one was correlated with negative psychological outcomes. Furthermore, positive affect was the most important psychological outcome related to COVID-19-related media exposure.

Indexed as

COVID-19Media contentMental healthNetwork analysisPositive affect

Identifiers

PMID35095352
PMCPMC8783777

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.