Facing Your Fears in autistic youth with co-occurring psychiatric conditions: Reductions in symptoms of anxiety and depression
Bibliographic record
Abstract
Autistic youth often experience psychiatric complexity, with up to 70 % of youth experiencing at least one mental health issue, and as many as 41 % experiencing two or more issues simultaneously, or complex psychiatric comorbidity. Adapted cognitive behaviour therapy (CBT) for anxiety is effective in reducing anxiety symptoms in autistic youth who have multiple anxiety conditions, as well as other psychiatric conditions (e.g., attention deficit hyperactivity disorder ; ADHD). However, few studies have examined the secondary impact of CBT for anxiety on depressive symptoms, despite the high prevalence of depression in autistic youth. Additionally, it is unknown how psychiatric complexity may impact, or moderate, autistic youth’s response to treatment. Fifty-one autistic youth (8–13 years old) participated in an adapted CBT intervention for anxiety (Facing Your Fears). Participants were categorized into one of three groups of increasing psychiatric complexity (Anxiety only; Anxiety + ADHD; and Anxiety + other mental health conditions). Paired sample t-tests and linear mixed models highlighted that autistic youth generally benefitted from CBT for anxiety, as seen in reductions of anxiety and depressive symptoms. Caregivers of youth in all diagnostic groups reported a significant reduction in anxiety symptoms and depression at post-treatment; however, only youth in the Anxiety only group, or those with less psychiatric complexity, reported a reduction in anxiety. Autistic youth did not report any changes in depressive symptoms, regardless of psychiatric complexity. Further study is needed to determine if symptom reductions continue overtime, and whether participating in adapted CBT for anxiety is associated with reductions in other mental health symptoms.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".