Gender Differences in the Prediction of Acute Stress Disorder From Peritraumatic Dissociation and Distress Among Victims of Violent Crimes
Bibliographic record
Abstract
Peritraumatic dissociation and distress are strong predictors of acute stress disorder (ASD) and posttraumatic stress disorder (PTSD) development. However, there is limited data concerning gender differences in these relations, particularly among victims of violent crimes (VVC). The objective of this study is to examine whether peritraumatic dissociation and distress predict the number of ASD symptoms differently for men and women VVC. In all, 162 adults (97 women, M age = 39.6 years), 63% of whom experienced physical assaults, completed the Acute Stress Disorder Interview, the Peritraumatic Dissociative Experience Questionnaire, and the Peritraumatic Distress Inventory. Analyses included t tests and multiple hierarchical regressions models controlling for known PTSD risk factors. The regression model showed dissociation and distress to be significant predictors of ASD for both men and women (β = .349 and β =.312 respectively; all p < .001). A significant three-way interaction was also observed between peritraumatic distress (PDI), past potentially traumatic experiences, and gender. In simple slopes analyses, the combination of high levels of PDI and of a high number of past potentially traumatic events were associated with greater risk of ASD in men only ( b = 3.78, p < .001). However, women experienced greater PDI, t(157) = 5.844, p = .005, than men, and elevated distress was associated with more ASD symptoms independently of past traumatic events. Gender differences were revealed as a function of past potentially traumatic experiences. There is a cumulative impact of past potential traumas and current distress that predicts ASD in men, while in women, it contributes to ASD via increased distress.
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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 | 0.000 |
| 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) | 0.001 | 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".