Depression, PTSD, and Comorbidity Related to Intimate Partner Violence in Civilian and Military Women
Bibliographic record
Abstract
The mental health consequences for women who have experienced intimate partner violence (IPV), such as major depressive disorder (MDD) and posttraumatic stress disorder (PTSD) and especially their comorbidity, have received little attention in large-scale studies and treatment protocols for affected populations. We compared the association of PTSD, MDD, and PTSD/MDD comorbidity to IPV in two large cohorts, one of military and the other of civilian women. The adjusted prevalence of mental health symptoms, especially PTSD, was higher among abused than nonabused women in both samples. Mental health symptoms were also higher among the civilian sample compared to the military sample. Approximately one-third (34%) of the abused civilian women and one-fourth (25%) of the abused military women had symptoms that met criteria for at least one of the three diagnostic categories employed in this study, compared to 18% and 15% of nonabused women in the two groups. Comorbidity of PTSD and depression affected 19.7% of the civilian abused women versus 4.5% of nonabused civilian women, whereas for active duty military women, the prevalence was 4.6% and 4.2% for abused and nonabused, respectively. To better understand the mental health consequences of IPV and to design the most effective treatment and prevention programs, it is important to examine the presence of comorbidities between mental health disorders.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".