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Record W2163236518 · doi:10.1177/1088357614547808

Parenting Stress as a Correlate of Cognitive Behavior Therapy Responsiveness in Children With Autism Spectrum Disorders and Anxiety

2014· article· en· W2163236518 on OpenAlexaff
Jonathan A. Weiss, Michelle A. Viecili, Yvonne Bohr

Bibliographic record

VenueFocus on Autism and Other Developmental Disabilities · 2014
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsYork University
FundersMultidisciplinary University Research Initiative
KeywordsAnxietyAutismPsychologyClinical psychologyAutism spectrum disorderCoping (psychology)CognitionCognitive restructuringSpecific phobiaIntervention (counseling)Developmental psychologyAnxiety disorderPsychiatry

Abstract

fetched live from OpenAlex

Children with autism spectrum disorder (ASD) often show high rates of anxiety, and cognitive behavior therapy (CBT) is recognized as an emerging evidence-based practice. Eighteen children (8–12 years of age, M = 9.5, SD = 1.34; male: n = 15) with ASD and significant anxiety problems participated in a 12-session group “Coping Cat” intervention together with their parents. Statistically significant reductions were noted across measures of parent-reported child anxiety, with 50% of children demonstrating clinically meaningful improvements using the conservative Reliable Change Index. Significant correlations were found between change in parenting stress and change in child anxiety from pre- to post-treatment. These results are applicable to the community service sector, where the Coping Cat program is commonly utilized. Due to the high prevalence of anxiety disorders in children with ASD, further research is needed to advance capacity building to help meet the significant needs of youth with ASD and anxiety.

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)0.005
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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.015
GPT teacher head0.262
Teacher spread0.247 · 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

Citations23
Published2014
Admission routes1
Has abstractyes

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