Personality Traits as Predictors of Inpatient Aggression in a High-Security Forensic Psychiatric Setting: Prospective Evaluation of the PCL-R and IPDE Dimension Ratings
Bibliographic record
Abstract
The Dangerous and Severe Personality Disorder (DSPD) initiative in England and Wales provides specialized care to high-risk offenders with mental disorders. This study investigated the predictive utility of personality traits, assessed using the Psychopathy Checklist-Revised (PCL-R) and the International Personality Disorder Examination, with 44 consecutive admissions to the DSPD unit at a high-security forensic psychiatric hospital. Incidents of interpersonal physical aggression (IPA) were observed for 39% of the sample over an average 1.5-year period following admission. Histrionic personality disorder (PD) predicted IPA, and Histrionic, Borderline, and Antisocial PDs all predicted repetitive (2+ incidents of) IPA. PCL-R Factor 1 and Facets 1 and 2 were also significant predictors of IPA. PCL-R Factor 1 and Histrionic PD scores were significantly associated with imminence of IPA. Results were discussed in terms of the utility of personality traits in risk assessment and treatment of specially selected high-risk forensic psychiatric patients in secure settings.
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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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".