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Record W2069847213 · doi:10.1037/a0019858

Subtypes of partner violence perpetrators among male and female psychiatric patients.

2010· article· en· W2069847213 on OpenAlexafffund
Zach Walsh, Marc T. Swogger, Brian P. O’Connor, Yael Chatav Schonbrun, M. Tracie Shea, Gregory L. Stuart

Bibliographic record

VenueJournal of Abnormal Psychology · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsPsychologyDomestic violencePsychopathologyGeneralizability theoryClinical psychologyPsychiatryIntimate partnerPoison controlSuicide preventionForensic psychiatryInjury preventionDevelopmental psychologyMedicineMedical emergency

Abstract

fetched live from OpenAlex

The goal of this naturalistic study was to examine heterogeneity among female and male civil psychiatric patients with a history of intimate partner violence (IPV) perpetration. Participants were 567 patients drawn from the MacArthur Violence Risk Assessment Study (J. Monahan et al., 2001). The authors examined subtype composition among 138 women and 93 men with positive histories of IPV and compared these groups with 111 women and 225 men with no histories of IPV. Findings for men and women were consistent with reports from studies of male perpetrators in forensic and community settings in that generally violent/antisocial, borderline/dysphoric, and family only/low-psychopathology subtypes of perpetrators were identified in both men and women. This study provides preliminary evidence for the generalizability of typologies derived from nonpsychiatric partner violence perpetrators to psychiatric populations and suggests that typologies derived from studies of male IPV perpetrators may provide useful guidance for the investigation of female IPV perpetration.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.323
Teacher spread0.310 · 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 source (direct Gemma or distilled Codex), 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

Citations88
Published2010
Admission routes2
Has abstractyes

Explore more

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