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Record W2289684752 · doi:10.1093/jpepsy/jsv085

Investigating a Proposed Model of Social Competence in Children With Traumatic Brain Injuries

2015· article· en· W2289684752 on OpenAlexaff
Sara Heverly-Fitt, Kenneth H. Rubin, Maureen Dennis, H. Gerry Taylor, Terry Stancin, Cynthia A. Gerhardt, Kathryn Vannatta, Erin D. Bigler, Keith Owen Yeates

Bibliographic record

VenueJournal of Pediatric Psychology · 2015
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsAlberta Children's HospitalUniversity of CalgaryHospital for Sick Children
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Child Health and Human DevelopmentNational Institutes of Health
KeywordsSocial competencePsychologyPsychological interventionDevelopmental psychologyTraumatic brain injuryCompetence (human resources)Peer groupClinical psychologyPoison controlMedicineSocial changePsychiatrySocial psychology

Abstract

fetched live from OpenAlex

OBJECTIVE: The goal of the current study was to test a proposed model of social competence for children who have suffered a traumatic brain injury (TBI). We hypothesized that both peer and teacher reports of social behavior would mediate the relation between intraindividual characteristics (e.g., executive function) and peer acceptance. METHODS: Participants were 52 children with TBI (M age = 10.29; M time after injury: 2.46 years). Severity of TBI ranged from complicated mild to severe. Classroom and laboratory measures were used to assess executive function, social behavior, and peer acceptance. RESULTS: Analyses revealed that peer reports of social behavior were a better mediator than teacher reports of the associations between executive function, social behaviors, and peer acceptance. DISCUSSION: The results underscore the importance of including peer reports of social behavior when developing interventions designed to improve the social, emotional, and behavioral outcomes of children with TBI.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.021
Threshold uncertainty score0.414

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.138
GPT teacher head0.402
Teacher spread0.265 · 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 teacher head, 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

Citations17
Published2015
Admission routes1
Has abstractyes

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