A randomized waitlist‐controlled trial of cognitive behavior therapy to improve emotion regulation in children with autism
Bibliographic record
Abstract
BACKGROUND: Mental health problems are common among individuals with autism spectrum disorder (ASD), and difficulties with emotion regulation processes may underlie these issues. Cognitive behavior therapy (CBT) is considered an efficacious treatment for anxiety in children with ASD. Additional research is needed to examine the efficacy of a transdiagnostic treatment approach, whereby the same treatment can be applied to multiple emotional problems, beyond solely anxiety. The purpose of the present study was to examine the efficacy of a manualized and individually delivered 10-session, transdiagnostic CBT intervention, aimed at improving emotion regulation and mental health difficulties in children with ASD. METHODS: Sixty-eight children (M age = 9.75, SD = 1.27) and their parents participated in the study, randomly allocated to either a treatment immediate (n = 35) or waitlist control condition (n = 33) (ISRCTN #67079741). Parent-, child-, and clinician-reported measures of emotion regulation and mental health were administered at baseline, postintervention/postwaitlist, and at 10-week follow-up. RESULTS: Children in the treatment immediate condition demonstrated significant improvements on measures of emotion regulation (i.e., emotionality, emotion regulation abilities with social skills) and aspects of psychopathology (i.e., a composite measure of internalizing and externalizing symptoms, adaptive behaviors) compared to those in the waitlist control condition. Treatment gains were maintained at follow-up. CONCLUSIONS: This study is the first transdiagnostic CBT efficacy trial for children with ASD. Additional investigations are needed to further establish its relative efficacy compared to more traditional models of CBT for children with ASD and other neurodevelopmental conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".