Predicting Which Clinically Documented Incidents of Aggression Lead to Findings of Guilt in a Forensic Psychiatric Sample
Bibliographic record
Abstract
This study identified factors that predicted which of 713 clinically documented incidents of aggression—threats to kill, assault, or sexual assault—committed by 404 forensic psychiatric patients were linked to court findings of guilt. Individuals had, on average, 1.7 aggressive incidents and were found guilty of an average of 0.3 offenses against persons during the study period. Aggressive incidents were mostly assaults, followed by uttering death threats, and sexual assaults. The victims of aggressive incidents were mainly other patients or staff, but some incidents involved family members or friends (16%) and strangers (14%). Most of the aggressive incidents (84%) did not lead to findings of guilt. Incidents of aggression linked to court findings were significantly associated with province; personality disorder; fewer prior aggressive incidents; and incidents involving strangers compared to staff or co-patients or to family or friends. These findings have implications for research in terms of understanding how criminal records underestimate histories of aggression. These findings also point to the need for the development of more consistent policies and procedures for responding to patient aggression, including when it is necessary or productive to report to police.
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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 | 0.010 |
| 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 | 0.001 | 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".