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Record W2612436412 · doi:10.1177/0829573517707907

Executive Function, Social Emotional Learning, and Social Competence in School-Aged Boys With Autism Spectrum Disorder

2017· article· en· W2612436412 on OpenAlexaff
Nathalie Catherine Marie Berard, Lynn Loutzenhiser, Phillip R. Sevigny, Dennis P. Alfano

Bibliographic record

VenueCanadian Journal of School Psychology · 2017
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsPsychologySocial competenceAutism spectrum disorderAutismDevelopmental psychologyPsychological interventionCompetence (human resources)Social skillsSocial emotional learningCognitionExecutive functionsSocial changeSocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

Autism Spectrum Disorder (ASD) is an aetiologically complex neurodevelopmental disorder characterized by deficits in social functioning. Children with ASD display a wide range of social competence and more variability in social domains as compared with either communication or repetitive behaviour domains. There is limited understanding of factors that contribute to the heterogeneity of social abilities in ASD. A modified version of McKown and colleagues’ social competence model was used to examine social competence in 49 8- to 13-year-old boys with ASD without cognitive disability. The relations between executive function (EF), social emotional learning (SEL), and parent reports of child social competence were examined. Results showed that EF but not SEL predicted parent-reported child social competence. Although many interventions target SEL skills, these findings support specifically targeting EF in both assessment and interventions of school-aged children with ASD.

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 score0.029
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
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 head0.033
GPT teacher head0.322
Teacher spread0.289 · 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

Citations12
Published2017
Admission routes1
Has abstractyes

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