Role of the Working Alliance on Treatment Outcome in Tailored Internet-Based Cognitive Behavioural Therapy for Anxiety Disorders: Randomized Controlled Pilot Trial
Bibliographic record
Abstract
BACKGROUND: Internet-based cognitive behavioral therapy (ICBT) is a form of guided self-help that has been found to be effective for addressing several problems. The target for this type of therapy is usually restricted to one specific disorder. Tailoring the treatment widens the scope of ICBT in that it can address comorbid conditions directly. OBJECTIVES: The working, or therapeutic, alliance has been found to predict outcome in studies of face-to-face therapy. The extent to which these findings apply to ICBT is largely unknown. We therefore decided to find out whether the working alliance could predict outcome in tailored ICBT for anxiety disorders. METHODS: Data were obtained from the treatment group (n=27) in a randomized controlled trial aiming to test the effects of tailored ICBT for anxiety disorders. The forthcoming study was designed to test the hypothesis that the working alliance measured both pre-treatment and early in treatment (week 3) can predict treatment outcome as measured by the Clinical Outcomes in Routine Evaluation-Outcome Measure (CORE-OM) in a heterogeneous group of patients with anxiety disorders (n=27). RESULTS: Working alliance measured at week 3 into the treatment correlated significantly with the residual gain scores on the primary outcome measure (r=-.47, P=.019, n=25), while expected working alliance pre-treatment did not (r=-.17, P=.42, n=27). CONCLUSIONS: These results raise questions about the importance of working alliance in ICBT treatments, and suggest that the working alliance could be important in ICBT.
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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".