The Effect of Therapeutic Alliance on Dropout in Cognitive Processing Therapy for Posttraumatic Stress Disorder
Bibliographic record
Abstract
Abstract A substantial number of individuals who undergo cognitive processing therapy (CPT) for posttraumatic stress disorder (PTSD) drop out before receiving a full course of treatment. Therapeutic alliance, defined as the working relationship between the therapist and client, is a dynamic process within therapy that may change over time. Research suggests that therapeutic alliance is associated with dropout in various treatments. However, no studies have yet examined the association between therapeutic alliance and dropout in CPT, and few studies have examined therapeutic alliance longitudinally over the course of treatment. Examining alliance in CPT through different methods may increase clinicians’ understanding of how to tailor interventions to prevent treatment dropout. The present study examined the association between therapeutic alliance and treatment dropout among 169 participants in a randomized implementation effectiveness trial. In total, 33.1% of clients dropped out over the course of CPT, and nearly half of these individuals dropped out during the first six sessions. Continuous‐time survival analysis results indicated that mean ratings of alliance significantly predicted treatment dropout, Wald χ2(1, N = 167) = 4.08, Exp(β) = .64, p = .043, whereas initial alliance, late alliance, and change in alliance over treatment did not. These findings suggest that overall therapeutic alliance is an important predictor of dropout from CPT.
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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 | 0.018 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".