Mental Disorders and Violence in a Total Birth Cohort
Bibliographic record
Abstract
BACKGROUND: We report on mental disorders and violence for a birth cohort of young adults, regardless of their contact with the health or justice systems. METHODS: We studied 961 young adults who constituted 94% of a total-city birth cohort in New Zealand, April 1, 1972, through March 31, 1973. Past-year prevalence of mental disorders was measured using standardized DSM-III-R interviews. Past-year violence was measured using self-reports of criminal offending and a search of official conviction records. We also tested whether substance use before the violent offense, adolescent excessive perceptions of threat, and a juvenile history of conduct disorder accounted for the link between mental disorders and violence. RESULTS: Individuals meeting diagnostic criteria for alcohol dependence, marijuana dependence, and schizophrenia-spectrum disorder were 1.9 (95% confidence interval [CI], 1.0-3.5), 3.8 (95% CI, 2.2-6.8), and 2.5 (95% CI, 1.1-5.7) times, respectively, more likely than control subjects to be violent. Persons with at least 1 of these 3 disorders constituted one fifth of the sample, but they accounted for half of the sample's violent crimes (10% of violence risk was uniquely attributable to schizophrenia-spectrum disorder). Among alcohol-dependent individuals, violence was best explained by substance use before the offense; among marijuana-dependent individuals, by a juvenile history of conduct disorder; and among individuals with schizophrenia-spectrum disorder, by excessive perceptions of threat and a history of conduct disorder. CONCLUSIONS: In the age group committing most violent incidents, individuals with mental disorders account for a considerable amount of violence in the community. Different mental disorders are linked to violence via different core explanations, suggesting multiple-targeted prevention strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".