Adaptation of Cognitive Behavior Therapy for Autistic Children During the Pandemic: A Mixed-Methods Program Evaluation
Bibliographic record
Abstract
Distancing requirements due to the pandemic have halted many in-person therapeutic programs, including cognitive behavior therapy (CBT), increasing the likelihood that autistic children with mental health problems will struggle without adequate access to evidence-based care. Policies meant to limit the spread of COVID-19 have inadvertently exacerbated the difficulties experienced by autistic children and further exposed them to vulnerabilities that will impact their mental health. In response, interventions have been adapted for remote delivery. There is limited evidence of the acceptability, feasibility, and clinical utility for treating mental health challenges in autistic children through an online medium, within the context of a pandemic. The current study used an explanatory sequential mixed methods design to assess parents’ experience as they participated in an adapted manualized CBT program (Secret Agent Society: Operation Regulation, SAS:OR; Beaumont, 2013) with their autistic child. Parents reported child-related behavioral changes in pre- and post-program surveys, and both parents and therapists were interviewed about their experience through the program. The quantitative findings suggest that children learned new emotion regulation through online participation, and parents were satisfied with the program. The qualitative data supported the quantitative findings and provided new insight into factors that facilitated child engagement or made participation challenging. Overall, the findings suggest that adapted online CBT programs for autistic children can have clinical utility, and further research is needed to determine their efficacy.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".