Efficacy of cognitive behavioral therapies with a trauma focus for posttraumatic stress disorder: An individual participant data meta-analysis.
Bibliographic record
Abstract
OBJECTIVE: This individual participant data meta-analysis aimed to investigate the effectiveness of cognitive behavioral therapy with a trauma focus (CBT-TF) for posttraumatic stress disorder (PTSD). Furthermore, we examined the effect of moderators on PTSD symptom severity. METHOD: This study included randomized controlled trials comparing CBT-TF to an inactive or active comparison group for adults with PTSD. The primary and secondary outcomes were PTSD symptom severity and remission, respectively. Moderators included sociodemographic and clinical variables. RESULTS: Twelve studies compared CBT-TF with inactive (n = 625) and 11 with active comparison conditions (n = 706). The one-stage individual participant data meta-analysis found that CBT-TF was more effective than inactive comparison conditions (β = -0.78; OR = 2.34) and not significantly different from active comparison conditions (β = 0.02; OR = 0.53) in reducing PTSD symptom severity and achieving PTSD remission, respectively. When comparing CBT-TF with inactive treatments, moderator analysis found that divorced participants had greater PTSD symptoms postintervention following CBT-TF than participants who were single, cohabitating, or married receiving CBT-TF, both in the completer (β = 0.93) and full-sample (β = 0.59) analyses. For the active treatment comparison, moderator analysis found that participants taking psychotropic medication had lower PTSD symptoms following CBT-TF than those not taking psychotropic medication in the completer analysis (β = -0.39). CONCLUSION: Based on our moderator analyses, further research is needed to understand the effect of psychotropic medication on the CBT-TF intervention process. Moreover, divorced participants with PTSD receiving CBT-TF might benefit from enhanced support. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.049 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".