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Record W2169171260 · doi:10.1177/1362361313518124

Improving transportability of a cognitive-behavioral treatment intervention for anxiety in youth with autism spectrum disorders: Results from a US–Canada collaboration

2014· article· en· W2169171260 on OpenAlexaffabout
Judy Reaven, Audrey Blakeley‐Smith, Tricia L. Beattie, April Sullivan, Eric J. Moody, Jessica Stern, Susan Hepburn, Isabel M. Smith

Bibliographic record

VenueAutism · 2014
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsDalhousie UniversityIzaak Walton Killam Health Centre
FundersNational Institutes of HealthNational Institute of Mental HealthU.S. Department of Health and Human Services
KeywordsAutismAnxietyFidelityPsychologyIntervention (counseling)Clinical psychologyAutism spectrum disorderCognitive behavioral therapyCognitionPsychiatry

Abstract

fetched live from OpenAlex

Anxiety disorders frequently co-occur in youth with autism spectrum disorders. In addition to developing efficacious treatments for anxiety in children with autism spectrum disorders, it is important to examine the transportability of these treatments to real-world settings. Study aims were to (a) train clinicians to deliver Facing Your Fears: Group Therapy for Managing Anxiety in Children with High-Functioning Autism Spectrum Disorders to fidelity and (b) examine feasibility of the program for novel settings. A secondary aim was to examine preliminary youth treatment outcome. Results indicated that clinicians obtained excellent fidelity following a workshop and ongoing consultation. Acceptability ratings indicated that Facing Your Fears Therapy was viewed favorably, and critiques were incorporated into program revisions. Meaningful reductions in anxiety were reported posttreatment for 53% of children. Results support the initial effectiveness and transportability of Facing Your Fears Therapy in new clinical settings.

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)0.005
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.166

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.288
Teacher spread0.263 · 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 designNon-randomized trial
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

Citations38
Published2014
Admission routes2
Has abstractyes

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