Massed cognitive processing therapy for posttraumatic stress disorder in women survivors of intimate partner violence.
Bibliographic record
Abstract
OBJECTIVE: Survivors of intimate partner violence (IPV) report significant trauma histories, high rates of posttraumatic stress disorder (PTSD), head injuries and comorbid disorders, and multiple barriers to treatment that often preclude the regular attendance and engagement required in typical therapy protocols. The significant challenges faced by IPV survivors needing treatment may be ameliorated by condensing effective treatments for PTSD, such as cognitive processing therapy (CPT), in an accelerated delivery timeline. METHOD: Using a multiple subject, single case design of six matched pairs of 12 female IPV survivors, we preliminarily tested the relative effectiveness of individual massed CPT delivered over 5 days (mCPT) as compared with standard CPT (sCPT) delivery in women IPV survivors. Assessments included full psychiatric diagnostic interviews, clinical interviews assessing trauma history and head injury prior to treatment, symptom monitoring during treatment, and full repeat assessments at 1 month and 3 months following treatment. RESULTS: s = 1.32-2.38). CONCLUSION: Overall, findings indicate mCPT appears effective in reducing psychological symptoms for women IPV survivors and suggest that condensed treatment is both palatable and feasible. Accelerated treatment delivery in this population may provide a necessary lifeline for women with IPV-related PTSD. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".