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Record W2965363849 · doi:10.1080/14999013.2019.1643428

The Perpetration of Violence and the Experience of Trauma: Exploring Predictors of PTSD Symptoms in Male Violent Offenders

2019· article· en· W2965363849 on OpenAlexaff
Marguerite Ternes, Barry S. Cooper, Dorothee Griesel

Bibliographic record

VenueInternational Journal of Forensic Mental Health · 2019
Typearticle
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsKelowna General HospitalUniversity of British ColumbiaSaint Mary's University
Fundersnot available
KeywordsPsychologyClinical psychologyPsychiatryPopulationPosttraumatic stressInjury preventionSuicide preventionPoison controlHuman factors and ergonomicsMedicineMedical emergency

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.294
Threshold uncertainty score0.193

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.051
GPT teacher head0.373
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2019
Admission routes1
Has abstractyes

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