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Record W2085786712 · doi:10.1007/s10803-015-2412-y

Thriving in Youth with Autism Spectrum Disorder and Intellectual Disability

2015· article· en· W2085786712 on OpenAlexafffund
Jonathan A. Weiss, Priscilla Burnham Riosa

Bibliographic record

VenueJournal of Autism and Developmental Disorders · 2015
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsYork University
FundersCanadian Institutes of Health ResearchHealth CanadaSinneave Family FoundationAutism Speaks
KeywordsThrivingAutism spectrum disorderPsychologyAutismIntellectual disabilityMental healthDevelopmental psychologyPsychological interventionCognitionClinical psychologyPsychiatryPsychotherapist

Abstract

fetched live from OpenAlex

Most research on mental health in individuals with autism spectrum disorder (ASD) and intellectual disability (ID) has focused on deficits. We examined individual (i.e., sociocommunicative skills, adaptive behavior, functional cognitive skills) and contextual (i.e., home, school, and community participation) correlates of thriving in 330 youth with ID and ASD compared to youth with ID only, 11-22 years of age (M = 16.74, SD = 2.95). Youth with ASD and ID were reported to thrive less than peers with ID only. Group differences in sociocommunicative ability and school participation mediated the relationship between ASD and less thriving. Research is needed to further elucidate a developmental-contextual framework that can inform interventions to promote mental health and wellness in individuals with ASD and ID.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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 science0.0000.001
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 head0.294
Teacher spread0.261 · 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

Citations47
Published2015
Admission routes2
Has abstractyes

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