Predictors and outcomes associated with therapeutic alliance in cognitive behaviour therapy for children with autism
Bibliographic record
Abstract
Therapeutic alliance is often an important aspect of psychotherapy, though it is rarely examined in clients with autism. This study aims to determine the child pre-treatment variables and treatment outcomes associated with early and late alliance in cognitive behaviour therapy targeting emotion regulation for children with autism. Data were collected from 48 children with autism who participated in a larger randomized-controlled trial. Pre-treatment child characteristics included child, parent, and clinician report of child emotional and behavioural functioning. Primary outcome measures included child and parent-reported emotion regulation. Therapeutic alliance (bond and task-collaboration) was measured using observational coding of early and late therapy sessions. Pre-treatment levels of child-reported emotion inhibition were associated with subsequent early and late bond. Pre-treatment levels of parent and child-reported emotion regulation were related to early and late task-collaboration. Late task-collaboration was also associated with pre-treatment levels of behavioural and emotional symptom severity. Task-collaboration in later sessions predicted improvements in parent-reported emotion regulation from pre- to post-therapy. Future research is needed to further examine the role of task-collaboration as a mechanism of treatment change in therapies for children with autism.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".