Psychopathic Traits, Risk and Protective Factors, and Attractiveness in Forensic Psychiatric Patients: Their Role in Review Board Dispositions
Bibliographic record
Abstract
Studies have demonstrated that the mere mention of criminal risk factors for future violent criminal behavior predicted decisions to detain or release not criminally responsible on account of mental disorder (NCRMD) patients following their review board hearing. We looked to further our understanding of review board decisions by assessing the influence of the mention of risk factors as well as psychopathic traits, protective factors, and the moderating effect of physical attractiveness. To this end, we coded the mention of risk factors, psychopathic traits, and protective factors in clinical reports of 90 former male NCRMD patients and rated their attractiveness on a scale of 1–10, of which 62 cases adhered to all inclusion and exclusion criteria. Analyses demonstrated that the mention of Psychopathy Checklist-Revised (PCL-R) items significantly increased the odds of being detained following a review board hearing, while attractiveness predicted decisions to discharge the patient. In addition, an interaction effect between PCL-R item-mentions and attractiveness was identified, such that highly attractive patients with high levels of PCL-R item-mentions were more likely to be detained following review board hearings. These results further our understanding of what factors influence review board decisions and demonstrate how extraneous factors can moderate the influence of risk-relevant information. Ultimately these results speak to the need to further educate decision-makers on evidence-based risk and protective factors and furthermore, how to avoid the pitfalls of allowing irrelevant information, such as physical attractiveness, from influencing such critical decisions affecting this vulnerable population.
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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 | 0.002 | 0.020 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".