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Record W2125824035 · doi:10.1002/bsl.678

Psychopathy in women: a review of its clinical usefulness for assessing risk for aggression and criminality

2005· review· en· W2125824035 on OpenAlexaff
Tonia L. Nicholls, James R. P. Ogloff, Johann Brink, Alicia Spidel

Bibliographic record

VenueBehavioral Sciences & the Law · 2005
Typereview
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversity of British ColumbiaRiverview Hospital
Fundersnot available
KeywordsPsychopathyPsychopathy ChecklistRecidivismPsychologyAggressionClinical psychologyPsychiatryPoison controlConceptualizationAntisocial personality disorderInjury preventionHuman factors and ergonomicsMedicineSocial psychologyPersonalityMedical emergency

Abstract

fetched live from OpenAlex

Despite a flurry of studies examining psychopathy in women, and the recent release of the second version of the Hare Psychopathy Checklist--Revised manual, there is still little consensus whether the lateral extension of the current conceptualization of psychopathy to women is appropriate. In particular, very little agreement exists concerning the clinical utility of the Hare psychopathy measures to assess women's risk of future offending and violence. This article presents a comprehensive review of studies of the association between psychopathy, antisocial behavior, and violence, in diverse samples of women, and looks at similarities and differences between these constructs in males and females. Findings from inmates and offenders, civil and forensic psychiatric patients, substance abusers, and community samples indicate a consistently lower base rate of psychopathy among women than among men. With some exceptions, correlates of psychopathy in women relevant to risk assessments for crime and violence tend to be modest and significant, generally mirroring what we see in men. Clinicians and policy makers charged with the care and management of women at risk for criminal offending and violence are likely to find the PCL-R and PCL:SV have clinical utility; however, cautious application is called for and ongoing research is required.

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)0.012
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.979
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.379
GPT teacher head0.563
Teacher spread0.184 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations144
Published2005
Admission routes1
Has abstractyes

Explore more

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