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Record W3204649093 · doi:10.1177/10901981211035702

Developmental Associations Between Media Use and Adolescent Prosocial Behavior

2021· article· en· W3204649093 on OpenAlexaffabout
Caroline Fitzpatrick, Elroy Boers

Bibliographic record

VenueHealth Education & Behavior · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineUniversité Sainte-Anne
Fundersnot available
KeywordsProsocial behaviorPsychologyThe InternetVideo gameDevelopmental psychologyPopulationMultilevel modelLongitudinal studyScreen timeSocial psychologyDemographyMultimediaMedicinePhysical activity

Abstract

fetched live from OpenAlex

Youth today spend a tremendous amount of time with digital media. The purpose of the present study was to estimate developmental associations between screen media use between the ages of 15 and 17 and corresponding changes in prosocial behavior. Participants ( N = 1,509) were part of the Quebec Longitudinal Study of Child Development, a population-based study of children born in the province of Quebec, Canada. Youth self-reported internet and video game use and television or movies/DVD viewing, as well as prosocial behavior at the ages of 15 and 17. Analyses were conducted using multilevel linear modelling to account for between-, within-, and lagged-person effects. Internet and video game use accounted for less prosocial behavior at the within-person and lagged-person levels. Television use also accounted for lagged-person effects in prosocial behavior. Finally, internet use and television viewing contributed to between person differences in prosocial behavior. Our study presents strong statistical evidence that media use during adolescence can undermine the development of prosocial behavior.

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 score0.125
Threshold uncertainty score0.249

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.113
GPT teacher head0.395
Teacher spread0.281 · 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

Citations20
Published2021
Admission routes2
Has abstractyes

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