Psychiatric Disorder in Detained Male Adolescents as Risk Factor for Serious Recidivism
Bibliographic record
Abstract
OBJECTIVE: A growing body of research consistently shows that detained minors bear substantial mental health needs. However, the relation between mental disorder and criminal recidivism has largely remained unexplored. Our study examines whether psychiatric disorders increase the likelihood of recidivism after controlling for time at risk, criminal history, and the presence of other disorders. METHOD: Participants (n = 232) were detained male adolescents from all 3 youth detention centres in Flanders, Belgium, who were interviewed with the Diagnostic Interview Schedule for Children, Version IV. Two to 4 years later, information on serious recidivism was retrieved from the official judicial registration system. Serious recidivism was defined as having at least one arrest charge for violent, severe property crime, or substance-related offences. RESULTS: Serious recidivism was high, with 81% (n = 191) of the participants being rearrested. Psychiatric disorders predicted neither serious recidivism in general nor violent and severe property recidivism. However, other drug use disorder (OR 2.41; 95% CI 1.22 to 4.75) and general comorbidity (OR 2.64; 95% CI 1.40 to 4.99) were significantly predictive of substance-related recidivism. CONCLUSION: Common psychiatric disorders in detained male adolescents do not significantly increase the likelihood of subsequent arrests, with the exception that substance use disorders appear to increase the risk of later substance-related recidivism. Effective treatment of these disorders may prevent detained juveniles to experience the detrimental outcomes associated with substance-related crimes as adults (for example, mental illness).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".