Cognitive–behavioral therapy for PTSD and depression symptoms reduces risk for future intimate partner violence among interpersonal trauma survivors.
Bibliographic record
Abstract
OBJECTIVE: Women who develop symptoms of posttraumatic stress disorder (PTSD) and depression subsequent to interpersonal trauma are at heightened risk for future intimate partner violence (IPV) victimization. Cognitive-behavioral therapy (CBT) is effective in reducing PTSD and depression symptoms, yet limited research has investigated the effectiveness of CBT in reducing risk for future IPV among interpersonal trauma survivors. METHOD: This study examined the effect of CBT for PTSD and depressive symptoms on the risk of future IPV victimization in a sample of women survivors of interpersonal violence. The current sample included 150 women diagnosed with PTSD secondary to an array of interpersonal traumatic events; they were participating in a randomized clinical trial of different forms of cognitive processing therapy for the treatment of PTSD. Participants were assessed at 9 time points as part of the larger trial: pretreatment, 6 times during treatment, posttreatment, and 6-month follow-up. RESULTS: As hypothesized, reductions in PTSD and in depressive symptoms during treatment were associated with a decreased likelihood of IPV victimization at a 6-month follow-up even after controlling for recent IPV (i.e., IPV from a current partner within the year prior to beginning the study) and prior interpersonal traumas. CONCLUSIONS: These findings highlight the importance of identifying and treating PTSD and depressive symptoms among interpersonal trauma survivors as a method for reducing risk for future IPV.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".