Female Forensic Patients May Be an Atypical Sub-type of Females Presenting Aggressive and Antisocial Behavior
Bibliographic record
Abstract
The percentage of forensic psychiatric patients who are female varies from 5 to 13% in Europe, rises to 18% in England and Wales, and sits at 15% in Canada. Similarly, many fewer women than men are incarcerated in correctional facilities. While these statistics supposedly reflect less antisocial and aggressive behavior (AAB) among females than males, not all findings support this supposition. Data from prospective longitudinal studies show that aggressive and antisocial behavior onsets in childhood, and in a small group of females it remains stable across the life-span. Unlike similar males, few of these females are convicted of crimes. This article begins with a review of descriptive studies of females sentenced by criminal courts to treatment in forensic psychiatric hospitals and moves on to present evidence showing that most female AAB does not lead to criminal prosecution. Next, studies of female AAB are reviewed, noting that it onsets in early childhood and, that in a small group remains stable across the life-span. Subsequent sections of the article focus on the two most common mental disorders presented by female forensic patients, schizophrenia and borderline personality disorder, highlighting what is known about the sub-groups of women with these disorders who present AAB. The article concludes with recommendations for earlier identification by psychiatric services of women presenting mental disorders and AAB, treatments to reduce both the symptoms of their mental disorders and their life-long AAB, and the research that is needed in order to improve the effectiveness of these treatments. The real possibilities of prevention of the development of AAB, and even perhaps aspects of the mental disorders that plague female forensic patients, are described.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".