The role of sexual assault history and Posttraumatic Stress Disorder (PTSD) symptoms in online treatment for Sexual Interest/Arousal Disorder in women
Bibliographic record
Abstract
Cognitive-Behavioral Therapy (CBT) and Mindfulness-Based Therapy (MBT) are efficacious treatments for Sexual Interest/Arousal Disorder (SIAD) in women. Many women with SIAD have a history of sexual assault (SA), but the degree to which SA history predicts engagement in treatment of SIAD, or its efficacy, is generally unknown. It is also possible that treatment of SIAD may improve Posttraumatic Stress Disorder (PTSD) symptoms related to SA. We engaged in secondary analysis of a trial assessing an online intervention for SIAD (called eSense) to explore whether SA history predicted treatment engagement or outcomes, and whether PTSD symptoms improved. Women with SIAD were randomized to online CBT (n = 43), online MBT (n = 43), or a waitlist control (n = 43). Participants completed self-report measures of engagement, SIAD symptoms, and PTSD symptoms at baseline, mid-treatment, posttreatment, and 6-month posttreatment. SA history did not predict treatment engagement or changes in SIAD symptoms. Overall PTSD symptoms decreased in MBT over and above waitlist. Exploratory analyses including follow-up assessment suggested that, among SA survivors, PTSD symptoms improved most in CBT whereas, for those without SA history, improvement was greater in MBT. SA survivors can use and benefit from evidence-based online therapies, like eSense, for SIAD.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".