Cognitive–behavioural conjoint therapy versus prolonged exposure for PTSD in military service members and veterans: results and lessons from a randomized controlled trial
Bibliographic record
Abstract
Background: Military personnel and veterans are at heightened risk for exposure to traumatic events and posttraumatic stress disorder (PTSD), as well as intimate relationship problems associated with PTSD.Objective: The purpose of this study was to evaluate the relative efficacy of CBCT and PE in improving intimate relationship functioning in active duty military personnel or veterans and their intimate partners; both conditions were hypothesized to significantly improve PTSD. Method: In this study, 32 military service members or veterans with PTSD and their intimate partners were randomized to receive either Cognitive–Behavioral Conjoint Therapy for PTSD (n = 15; CBCT; [Monson, C. M., & Fredman, S. J. (2012). Cognitive-behavioral conjoint therapy for posttraumatic stress disorder: Harnessing the healing power of relationships. Guilford]), a trauma-focused couple therapy, or Prolonged Exposure (n = 17; PE; [Foa, E. B., Hembree, E. A., Dancu, C. V., Peterson, A. L., Cigrang, J. A., & Riggs, D. S. (2008). Prolonged exposure treatment for combat-related stress disorders – provider’s treatment manual [unpublished]. Department of Psychiatry, University of Pennsylvania]), a front-line evidence-based individual treatment for PTSD.Results: There were significant challenges with recruitment and a significant difference in dropout from treatment for the two therapies (65% for PE; 27% for CBCT). Treatment dropout was differentially related to pre-treatment relationship functioning; those with below average relationship functioning had higher dropout in PE compared with CBCT, whereas those with above average relationship functioning did not show differential dropout. In general, CBCT led to relational improvements, but this was not consistently found in PE. Clinician- and self-reported PTSD symptoms improved with both treatments.Conclusions: This study is the first to test a couple or family therapy against a well-established, front-line recommended treatment for PTSD, with expected superiority of CBCT over PE on relationship outcomes. Lessons learned in trial design, including considerations of equipoise, and the effects of differential dropout on trial analyses are discussed. This trial provides further support for the efficacy of CBCT in the treatment of PTSD and enhancement of intimate relationships.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".