The relationships between therapeutic alliance and internalizing and externalizing symptoms in Trauma-Focused Cognitive Behavioral Therapy
Bibliographic record
Abstract
Therapeutic alliance has been considered an important factor in child psychotherapy and is consistently associated with positive outcomes. Nevertheless, research on alliance in the context of child trauma therapy is very scarce. This study examined the relationships between child therapeutic alliance and psychopathology in an empirically supported child trauma therapy model designed to address issues related to trauma with children and their caregivers. Specifically, we examined the extent to which the child's psychopathology would predict the establishment of a positive alliance early in treatment, as well as the association between alliance and outcome. Participants were 95 children between the ages of 7 and 12 and their caregivers, who went through a community-based Trauma-Focused Cognitive Behavioral Therapy program in Canada. Caregivers filled out the CBCL prior to assessment and following treatment. Children and therapists completed an alliance measure (TASC) at three time points throughout treatment. Symptomatology and child gender emerged as important factors predicting alliance at the beginning of treatment. Girls and internalizing children developed stronger alliances early in treatment. In addition, a strong early alliance emerged as a significant predictor of improvement in internalizing symptoms at the end of treatment. Our findings indicate that symptomatology and gender influence the development of a strong alliance in trauma therapy. We suggest that clinicians should adjust therapeutic style to better engage boys and highly externalizing children in the early stages of therapy.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".