Treatment Engagement as a Predictor of Therapy Outcome Following Cognitive Behaviour Therapy for Autistic Children
Bibliographic record
Abstract
PURPOSE: Active engagement in one's therapy is a key contributor to successful outcomes. Research on child engagement in cognitive behaviour therapy (CBT) has largely focused on youth without autism. This longitudinal study examined multiple indicators of child engagement in relation to outcomes for autistic children who took part in CBT for emotion regulation. METHOD: = 9.58 years, SD = 1.44 years; 75% White). Indicators of child engagement included independent observer ratings of in-session involvement, as measured by the Child Involvement Rating Scale, and therapist ratings of the therapeutic relationship and homework completion using single-item measures. Indicators of engagement were measured at early (i.e., first third), middle (i.e., mid third), and late (i.e., final third) stages of treatment. Parent-reported emotion regulation was the primary treatment outcome, as measured by the Emotion Regulation Checklist. RESULTS: After controlling for pre-treatment scores, in-session involvement significantly predicted some aspects of post-treatment emotion regulation, whereas therapeutic relationship and homework completion did not. CONCLUSIONS: Child in-session involvement throughout therapy may be particularly relevant for treatment change. Addressing issues related to in-session involvement early in treatment may help to promote therapeutic success for autistic children.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".