Women with PTSD benefit more from psychotherapy than men.
Bibliographic record
Abstract
OBJECTIVE: This study aimed to examine possible gender differences in therapy gain in patients with posttraumatic stress disorder (PTSD). It also aimed to examine whether the gender effect could be explained by gender differences in dropout rates, trauma type (interpersonal/noninterpersonal), or context of the event (work-related/not work-related). METHOD: Seventy-one participants received 20-session cognitive-behavior therapy for PTSD. They were assessed pre- and posttreatment on primary and secondary outcome measures: PTSD symptoms, quality of life, avoidance, social support and positive reappraisal copings, and supportive and countersupportive interactions. RESULTS: Regression analysis showed that gender explained 6%-9% significant variance in the outcome: Women statistically benefited more from the treatment than men on quality of life (p < .05), avoidance (p < .01), and support seeking (p < .05) copings, supportive (p < .05), and countersupportive (p < .05) interactions. However, there was no statistically significant gender difference on PTSD symptoms and positive reappraisal coping. Dropout rate, trauma type, and context of trauma could not explain the gender differences. CONCLUSION: The results might explain ambiguous previous results on gender differences in therapy efficacy for PTSD, and highlight the importance of using multiple measurements in the evaluation of treatment outcome in PTSD. Further research is needed to explain the exact mechanisms behind women's getting more of therapy's secondary benefits. (PsycINFO Database Record
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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) | 0.014 | 0.002 |
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".