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Record W4404024958 · doi:10.22215/cujs.v3i1.4926

Functional Communication in Autistic Children Impacts Skill Development After Participation in a CBT-Based Program

2024· article· en· W4404024958 on OpenAlexaff
Sarah Foster, Stephanie H. Ameis, M. Ariel Cascio, Kylie M. Gray, Connor M. Kerns, Meng‐Chuan Lai, Johanna Lake, Éric Racine, Kendra Thomson, Jonathan A. Weiss, Vivian Lee

Bibliographic record

VenueCarleton undergraduate journal of science. · 2024
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsBrock UniversityMontreal Clinical Research InstituteYork UniversityUniversity of British ColumbiaCentre for Addiction and Mental HealthCarleton University
Fundersnot available
KeywordsPsychologyDevelopmental psychologyAutism

Abstract

fetched live from OpenAlex

Challenges with functional communication (e.g., language use in situational contexts), are a core feature of autism. Challenges in functional communication are related to social interaction difficulties and may impact engagement in interventions such as cognitive-behavioural therapy (CBT). CBT is a common intervention used to support emotion regulation and social skills development in autistic children. Programming relies on the participant’s ability to engage in complex language. The present study examined whether the functional communication level of autistic children (8-12 years) impacted social skills and emotion regulation development after participation in a CBT-based program. Repeated measures ANOVAs revealed no significant differences in emotion regulation, however, there was a significant main effect for social skills, based on functional communication levels. T-tests revealed significant improvements for all measures in the clinically significant functional communication group, but not the at-risk or non-significant groups. These results illustrate the importance of considering functional communication levels in autistic children.

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 score0.471
Threshold uncertainty score0.552

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.029
GPT teacher head0.339
Teacher spread0.310 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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