Psychopathy in women: a review of its clinical usefulness for assessing risk for aggression and criminality
Bibliographic record
Abstract
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.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".