Efficacy of Imagery Rehearsal Therapy and Cognitive Behavioral Therapy in Sexual Assault Victims With Posttraumatic Stress Disorder: A Randomized Controlled Trial
Bibliographic record
Abstract
Sleep disturbances are common among sexual assault victims with posttraumatic stress disorder (PTSD), but cognitive behavioral therapy (CBT) for PTSD does not directly address sleep-related symptoms. Trauma-related sleep disturbances are associated with more impairment and contribute to the maintenance of PTSD. In this study, we evaluated the efficacy of a combination of CBT and nightmare therapy (imagery rehearsal therapy; IRT) compared to CBT alone for the treatment of PTSD. We recruited 42 adult victims of sexual assault who were suffering from PTSD and randomly assigned them to either the experimental (IRT + CBT) or control condition (waiting period followed by CBT). After CBT, both groups demonstrated significant decreases in nighttime symptoms (except nightmare frequency) and PTSD symptoms and showed improvements in functional impairment and mental health, ds = 0.13-0.83, ps = .005-.008. Outcomes between the two groups did not differ significantly after CBT; however, we observed medium to medium-large differences between the control group and experimental group in terms of nighttime symptoms, ds = 0.45-0.63. Although results did not clearly establish the superiority of IRT + CBT over CBT alone, they demonstrated that IRT yielded greater improvement in nighttime symptoms than the waiting period, ds = 0.72-1.13, ps = .006-.047 for all interaction effects. Findings suggest that targeting nightmares at the beginning of treatment for PTSD may yield rapid improvement in nighttime symptoms. This strategy could be useful for patients with time or resource constraints or those for whom nightmares are the primary complaint.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".