A meta-analytic review of cognitive processing therapy for adults with posttraumatic stress disorder
Bibliographic record
Abstract
Numerous studies have demonstrated the efficacy of cognitive processing therapy (CPT) for treating posttraumatic stress disorder (PTSD). Two prior meta-analyses of studies are available but used approaches that limit conclusions that can be drawn regarding the impact of CPT on PTSD outcomes. The current meta-analysis reviewed outcomes of trials that tested the efficacy of CPT for PTSD in adults and evaluated potential moderators of outcomes. All published trials comparing CPT against an inactive control condition (i.e. psychological placebo or wait-list) or other active treatment for PTSD in adults were included, resulting in 11 studies with a total of 1130 participants. CPT outperformed inactive control conditions on PTSD outcome measures at posttreatment (mean Hedges' g = 1.24) and follow-up (mean Hedges' g = 0.90). The average CPT-treated participant fared better than 89% of those in inactive control conditions at posttreatment and 82% at follow-up. Results also showed that CPT outperformed inactive control conditions on non-PTSD outcome measures at posttreatment and follow-up and that CPT outperformed other active treatments at posttreatment but not at follow-up. Effect sizes of CPT on PTSD symptoms were not significantly moderated by participant age, number of treatment sessions, total sample size, length of follow-up, or group versus individual treatment; but, older studies had larger effect sizes and percent female sex moderated the effect of CPT on non-PTSD outcomes. These meta-analytic findings indicate that CPT is an effective PTSD treatment with lasting benefits across a range of outcomes.
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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 | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.026 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".