Depression, PTSD and alexithymia in victims of intimate partner violence: a case-control study
Bibliographic record
Abstract
Abstract Background Intimate partner violence (IPV) regards millions of women worldwide and can lead to serious psychopathological consequences. Objective We aimed to evaluate differences between a group of abused women and controls, and potential predictors of depression and PTSD in the IPV group. Methods We recruited 57 women who experienced IPV and 57 age-matched controls from the general population. After collecting socio-demographic characteristics, we administered the following scales: Hamilton Depression Rating Scale (HDRS), Davidson Trauma Scale (DTS), Toronto Alexithymia Scale (TAS-20) and Revised-Conflict Tactics Scale (CTS-2). Results Our results showed differences between women who experienced IPV and controls in the socio-economic status, employment and educational levels, childhood abuse and early terminations of pregnancy. Notably, the rates of depression, PTSD, and alexithymia were significantly different between the two groups. Linear regression models revealed that sexual coercion was an independent positive predictor of depressive symptoms, while alexithymia played a role in the development of PTSD in the group of abused women. Discussion Given the prevalence of depression and PTSD in victims of IPV, it is important to always investigate for IPV in women seeking for help in mental health services. Alexithymia in victims of IPV deserves to be further investigated by researchers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".