The Perpetration of Violence and the Experience of Trauma: Exploring Predictors of PTSD Symptoms in Male Violent Offenders
Bibliographic record
Abstract
Incarcerated offenders are more likely to experience Posttraumatic Stress Disorder (PTSD) and associated symptoms than the general population. PTSD may develop from a variety of events, including being a victim of violence, witnessing violence, or from committing a violent offense. This study examined symptoms and predictors of PTSD in 150 male violent offenders. Participants recalled acts of reactive and instrumental violence, poorly recalled violence, and subjectively disturbing events (e.g., victim of violence), and rated each event for symptoms of PTSD using the Impact of Events Scale (IES). Subjectively disturbing events were associated with higher IES scores than the acts of violence. Hierarchical linear modeling showed that more recent events were associated with a greater number of trauma symptoms and peritraumatic dissociation was positively associated with trauma symptoms. As well, trauma symptoms were more likely to develop if the victim was a family member or a friend, as compared to a stranger or acquaintance. These results support the need for trauma-informed assessment and treatment for offenders. Knowing more about the predictors of trauma symptoms is a first step in effectively treating PTSD in this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 |
| 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".