Quality of Life After Violent Crime: The Impact of Acute Stress Disorder, Posttraumatic Stress Disorder, and Other Consequences
Bibliographic record
Abstract
Abstract Victims of violent crime (VVC) are at risk of developing acute stress disorder (ASD) and subsequent posttraumatic stress disorder (PTSD). In addition, VVC are more likely to have low social support due to stigmatization and victim‐blaming, and PTSD is frequently associated with depression, anxiety, and impaired quality of life (QoL). The present study aimed to determine the impact of ASD, PTSD, depressive symptoms, anxiety symptoms, and perceived social support in relation to four domains of QoL among VVC. Individuals were recruited as part of a longitudinal study assessing the efficacy of a brief cognitive behavioral treatment for ASD. Participants (N = 127) were interviewed and completed self‐report measures within 30 days of experiencing a violent crime (T0) and at assessments 2 months (T1) and 6 months (T2) after the event. Depressive symptoms, ASD, and PTSD were found to be associated with lower QoL ratings in all four domains. Anxiety symptoms were found to be associated with lower ratings in the physical health and psychological QoL domains. Perceived social support was found to be associated with higher QoL ratings in all domains. The proportions of QoL variance explained by the combined fixed and random effects combined ranged from 70% to 79%. Future research considerations include an examination of how early interventions for VVC could prevent QoL deterioration by targeting ASD and PTSD development, depressive and anxiety symptoms, and social support.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".