A Public Health Perspective on Violent Offenses Among Persons With Mental Illness
Bibliographic record
Abstract
OBJECTIVE: This study reanalyzed existing data to assess the extent to which persons with mental illness might contribute to criminal violence in the community. METHODS: Data were examined from a representative sample of 1,151 remanded offenders who underwent a full structured diagnostic interview that was used to provide one-month prevalence rates of mental illnesses as defined by the Structured Clincal Interview for DSM-III-R. Diagnoses of interest were mood, psychotic, anxiety, psychoactive substance use, adjustment, and miscellaneous axis I disorders and axis II personality disorders. Criminological data describing the number of offenses against persons and property and the number of victimless crimes were abstracted from police arrest reports and warrants. A violent crime was defined as any crime against a person. RESULTS: The one-month prevalence of major mental and substance use disorders of newly admitted inmates was 61 percent. About 3 percent of violent offenses could be attributed to individuals who had a principal diagnosis of any non-substance use-related disorder. An additional 7 percent of violent offenses could be attributed to individuals who had a primary diagnosis of a substance use disorder. CONCLUSIONS: The results of the study support the hypothesis that people with mental and substance use disorders are not major contributors to police-identified criminal violence. Public perceptions of mentally ill persons as criminally dangerous appear to be greatly exaggerated.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.005 | 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".