Virtual delivery of group-based cognitive behavioral therapy for autistic children and youth during the COVID-19 pandemic was acceptable, feasible, and effective
Bibliographic record
Abstract
Anxiety challenges co-occur at a high rate in autistic children and youth (~ 50-79%), often with significant interference with daily functioning. Evidence-based interventions (e.g., cognitive behavioral therapy (CBT)-based approaches) are effective in treating anxiety disorders across populations. Facing your fears (FYF), a group-based CBT program modified for youth with ASD, yields positive outcomes in controlled research settings and community implementation, but access is constrained by limited system capacity and families' distance from specialized centers. COVID-19 spurred innovations in virtual delivery of care, generating possibilities for increased scalability of evidence-based treatments. This study investigated the feasibility, acceptability, and effectiveness of FYF when delivered virtually through a tertiary care hospital in Ontario. Data were collected over one year (N = 100 autistic children/youth aged 8-13 years and their caregivers). Significant improvements emerged in caregiver- and self-reported anxiety symptoms, and caregivers reported increased self-efficacy in supporting their child with their anxiety. Significant predictors of treatment response included youth baseline anxiety, level of adaptive functioning, ASD symptoms, and caregiver self-efficacy. Three COVID-related factors were small but significant contributors to the model. Virtual delivery of FYF is feasible and effective for treating elevated anxiety in autistic children/youth and may improve access.ClinicalTrials.gov identifier: NCT04666493.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".