Secondary individual outcomes following multicouple group therapy for posttraumatic stress disorder: An uncontrolled pilot study with military dyads
Bibliographic record
Abstract
Cognitive-behavioral conjoint therapy (CBCT) for posttraumatic stress disorder (PTSD) has demonstrated efficacy for improving PTSD and comorbid symptoms and relationship adjustment. To enhance treatment efficiency and scalability, we developed a 2-day, abbreviated, intensive, multicouple group version of CBCT for PTSD (AIM-CBCT for PTSD). Prior work demonstrated that AIM-CBCT for PTSD wasassociated with reductions in PTSD and comorbid symptoms in a sample of 24 post-9/11 active duty military or veteran couples who received the treatment in a retreat format over a single weekend. The current study investigated secondary outcomes regarding trauma-related cognitions, psychosocial impairment, and insomnia. For trauma-related cognitions, reductions were nonsignificant and small at 1-month follow-up, ds = -0.14 to -0.32. However, by 3-month follow-up, there were significant, medium effect size reductions in total trauma-related cognitions, d = -0.68, and negative views of self and others, ds = -0.64 and -0.57, respectively, relative to baseline. There was also a nonsignificant, small-to-medium effect-size reduction in self-blame, d = -0.43, p = .053, by 3-month follow-up. For psychosocial impairment, there were significant and medium-to-large and large effect size reductions by 1- and 3-month follow-ups, ds = -0.73 and -0.81, respectively. There were nonsignificant, small effect size reductions in insomnia at both 1- and 3-month follow-ups relative to baseline, ds = -0.30 and -0.34, respectively. These findings suggest that AIM-CBCT for PTSD is associated with reductions in maladaptive posttraumatic cognitions and psychosocial impairment but that adjunctive interventions may be needed to address insomnia.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".