The effect of early therapeutic alliance on treatment dropout in cognitive processing therapy and client factors as moderators of this relationship
Bibliographic record
Abstract
Although efficacious treatments, including Cognitive Processing Therapy (CPT), are available for treating Posttraumatic Stress Disorder (PTSD), a substantial number of clients do not receive a full course of CPT due to clients dropping out prematurely. Examining factors associated with treatment dropout may increase our understanding on how to tailor interventions to prevent treatment dropout. This study examined the relationship between early therapeutic alliance and treatment dropout, and client age and pretreatment PTSD symptom severity as predictors of dropout and moderators of the alliance-dropout association. Clients were part of a larger randomized implementation trial, and either began CPT and dropped out (n = 38) or completed 12 sessions of CPT (n = 74). Results indicated early therapeutic alliance did not significantly predict treatment dropout, and age and PTSD severity were not significant predictors or moderators of the alliance-dropout association. Clinical implications of the findings are discussed.
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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 | 0.039 | 0.121 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".