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Record W2991098148 · doi:10.1002/jcop.22293

Social media use and parent–child relationship: A cross‐sectional study of adolescents

2019· article· en· W2991098148 on OpenAlexaff
Hugues Sampasa‐Kanyinga, Gary S. Goldfield, Mila Kingsbury, Zahra M. Clayborne, Ian Colman

Bibliographic record

VenueJournal of Community Psychology · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsChildren's Hospital of Eastern OntarioUniversity of Ottawa
Fundersnot available
KeywordsDaughterOdds ratioOddsAssociation (psychology)DemographyConfidence intervalPsychologySocial mediaDevelopmental psychologyScreen timeMedicineLogistic regressionInternal medicineSociologyBiology

Abstract

fetched live from OpenAlex

We examined the association between social media use and parent-child relationship quality and tested whether this association is independent of total screen time. Data on 9,732 students (48.4% female) aged 11-20 years were obtained from a provincially representative school-based survey. Heavy use of social media (daily use of more than 2 hr) was associated with greater odds of negative relationships between mother-daughter (odds ratio [OR] = 1.79; 95% confidence interval [CI]: 1.27-2.52), father-daughter (OR = 1.56; 95% CI: 1.16-2.09), father-son (OR = 2.19; 95% CI: 1.58-3.05) but not mother-son (OR = 1.17; 95% CI: 0.88-1.55). Results were similar after further adjusting for total screen time. There were no significant associations between regular use of social media (2 hr or less) and parent-child relationships. These findings suggest that heavy use of social media is associated with negative parent-child relationships. Longitudinal research is necessary to disentangle the pathways between social media use and the parent-child relationship.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.140
GPT teacher head0.417
Teacher spread0.277 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations49
Published2019
Admission routes1
Has abstractyes

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