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Record W4388490970 · doi:10.1080/28324765.2023.2278879

Shyness and problematic internet use among adolescents and young adults: A systematic review and meta‐analysis

2023· review· en· W4388490970 on OpenAlexaff
Bowen Xiao, Natasha Parent, Claire Hein‐Salvi, Jennifer D. Shapka

Bibliographic record

VenueCogent Mental Health · 2023
Typereview
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsShynessModerationPsychologyThe InternetMeta-analysisYoung adultIntervention (counseling)Developmental psychologyClinical psychologyMedicineSocial psychologyPsychiatryWorld Wide WebAnxietyInternal medicine

Abstract

fetched live from OpenAlex

This study examined the association between shyness and problematic internet use among adolescents and young adults. A systematic search of relevant publications in English published before September 2022 yielded 35 studies in total (n = 26,708 individuals), which were systematically reviewed. Among them, 27 studies were included in the meta-analyses. Results from random-effects models indicated that shyness was positively associated with problematic internet use (μ̂ = 0.2753; 95% CI: 0.2409 to 0.3096). Moreover, results from the moderation analyses indicated that the relationship between shyness and problematic internet use was moderated by age group. Specifically, the effect size of shyness on problematic internet use seemed to be larger for young adults than for adolescents (Z = -2.25, p < .001). Our findings indicated that shy people, particularly shy young adults, are more likely to have problematic internet use, which provided useful information for future intervention.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.022
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0100.020
Bibliometrics0.0080.007
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.092
GPT teacher head0.399
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2023
Admission routes1
Has abstractyes

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