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Record W2156530240 · doi:10.1177/2167702613499734

Social Competence in Pediatric Traumatic Brain Injury

2013· article· en· W2156530240 on OpenAlexaff
Keith Owen Yeates, Erin D. Bigler, Tracy J. Abildskov, Maureen Dennis, Cynthia A. Gerhardt, Kathryn Vannatta, Kenneth H. Rubin, Terry Stancin, H. Gerry Taylor

Bibliographic record

VenueClinical Psychological Science · 2013
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsPsychologyPsychosocialFriendshipTraumatic brain injurySocial competencePsychopathologyDevelopmental psychologyClinical psychologyPeer victimizationCompetence (human resources)Poison controlInjury preventionPsychiatrySocial changeMedicineSocial psychology

Abstract

fetched live from OpenAlex

This study examined the associations among brain volumes, theory of mind (ToM), peer relationships, and psychosocial adjustment in children with traumatic brain injury (TBI). Participants included 8- to 13-year-old children, 82 with TBI and 61 with orthopedic injuries (OIs). Children completed three measures of ToM. Classmates provided ratings of participants’ peer relationships, acceptance, and friendships. Parents rated children’s psychosocial adjustment. MRI was used to determine brain volumes. Brain volumes were associated with ToM, which in turn was associated with peer rejection/victimization. Peer rejection/victimization in the classroom was associated with peer acceptance, friendship, social withdrawal, and general psychopathology. Brain volumes, ToM, peer relationships, and social adjustment show significant links among children with TBI and those with OI. The findings support a multilevel model of social competence in childhood 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 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)0.004
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.368
GPT teacher head0.567
Teacher spread0.199 · 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

Citations32
Published2013
Admission routes1
Has abstractyes

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