Individual, Service, and Neighborhood Predictors of Aggression Among Persons With Mental Disorders
Bibliographic record
Abstract
We examined the predictive validity of individual, service, and neighborhood factors for aggression by 1,491 forensic clients found Not Criminally Responsible on account of Mental Disorder (NCRMD), under the jurisdiction of a Review Board, and thus subject to supervision conditions. Younger patient age and personality disorder diagnosis were associated with both clinically documented aggression and recidivism. Medication adherence was related to clinically documented aggression, but not criminal recidivism. Number of reports from an institution (possibly reflecting assessor or institutional experience with NCRMD assessments) did not predict clinically documented aggression, but more comprehensive risk reports were associated with fewer such clinical incidents. More community mental health resources within a 45-min drive of an individual’s residence were associated with less recidivism, but not less clinically documented aggression. We conclude that extra-individual factors are related to aggression, the neighborhood to which forensic clients return matters, and effects can differ for criminal recidivism versus clinically documented aggression.
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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 | 0.003 |
| 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) | 0.002 | 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".