Conduct Disorder and Antisocial Personality Disorder in Persons With Severe Psychiatric and Substance Use Disorders
Bibliographic record
Abstract
Conduct disorder (CD) and antisocial personality disorder (ASPD) are established risk factors for substance use disorders in both the general population and among persons with schizophrenia and other severe mental illnesses. Among clients with substance use disorders in the general population, CD and ASPD are associated with more severe problems and criminal justice involvement, but little research has examined their correlates in clients with dual disorders. To address this question, we compared the demographic, substance abuse, clinical, homelessness, sexual risk, and criminal justice characteristics of 178 dual disorder clients living in 2 urban areas between 4 groups: No CD/ASPD, CD Only, Adult ASPD Only, and Full ASPD. Clients in the Adult ASPD Only group tended to have the most severe drug abuse severity, the most extensive homelessness, and the most lifetime sexual partners, followed by the Full ASPD group, compared with the other 2 groups. However, clients with Full ASPD had the most criminal justice involvement, especially with respect to violent charges and convictions. The results suggest that a late-onset ASPD subtype may develop in clients with severe mental illness secondary to substance abuse, but that much criminal behavior in clients with dual disorders may be due to the early onset of the full ASPD syndrome in this population and not the effects of substance use disorders.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".