Evidence map›Paper›PMID 34019485›Full record

ArticleJournal of behavioral addictions2021

Problematic Internet use among residential college students during the COVID-19 lockdown: A social network analysis approach.

Yiwei Xia, Yanying Fan, Tzu-Hsuan Liu, Zhihao Ma

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Article in Journal of behavioral addictions, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Yiwei Xia1School of Law, Southwestern University of Finance and Economics, Chengdu 611130, China.
Yanying Fan1School of Law, Southwestern University of Finance and Economics, Chengdu 611130, China.
Tzu-Hsuan Liu2School of Political Science and Public Administration, Huaqiao University, Quanzhou 362021, China.
Zhihao Ma3Computational Communication Collaboratory, School of Journalism and Communication, Nanjing University, Nanjing 210023, China.ORCID 0000-0001-7983-4136

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

aimsDuring the COVID-19 lockdown, problematic Internet use (PIU) has become a serious issue among residential college students, who remain physically isolated from off-campus society. This study constructs an integrated model to investigate the influencing mechanisms of internal locus of control (LOC) and objective peer effects.

methodsResidential college students (n = 494) were surveyed from a single department of a Chinese university. An item from the World Value Survey was employed to measure internal LOC, while objective peer effects were assessed via friends' mutual nominations. Finally, PIU was measured using Young's Internet Addiction Tests, while a social network analysis and logit regression were combined to estimate various factors' effects on PIU.

resultsIn our sample, the prevalence rate of PIU was 30.6%, and while internal LOC was a protective factor for PIU, its protective role was diluted when exposed to a peer environment with high PIU prevalence. Furthermore, indegree performed contrasting roles on PIU under various network conditions. It acted as a protective factor when exposed to a low prevalence of PIU in a peer environment; however, it became a risk factor when PIU peers were prevalent. Lastly, the protective efficacy of betweenness was activated when individuals had more than one PIU friend. DISCUSSION AND

conclusionsFurther intervention studies focusing on individuals with a weak internal LOC are recommended during the lockdown. Additionally, interventions that consider the network structures carefully, may enhance the prevention of PIU.

Indexed as

Behavior, AddictiveCOVID-19Communicable Disease ControlHumansInternetInternet UseSocial Network AnalysisStudentsCOVID-19internal locus of controlobjective peer effectsproblematic Internet useresidential college studentssocial network analysis

Identifiers

PMID34019485
PMCPMC8996801

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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.