Young Offenders in Custody: An International Comparison of Mental Health Services
Bibliographic record
Abstract
Over the last decade interest in the mental health of young offenders has soared. Given evidence that rates of mental disorders are remarkably high in adolescent offenders, countries have initiated efforts to improve the identification and treatment of mental illness in detained youth. Research examining current mental health screening, assessment, and treatment services has focused largely on American sites; however, there is much to be gained from an international perspective. This article discusses international findings regarding the prevalence of mental illness in detained youth, examines the extent to which countries protect incarcerated youths’ right to treatment, and compares countries’ provision of screening, assessment, and treatment services to youth in custody and the community. Considerable variability is found to exist across countries and despite some advances in practice—particularly in the area of mental health screening—significant gaps remain between best and actual practices. To ensure that young offenders with mental illness receive adequate treatment, local, in-depth reviews of the mental health services provided to young offenders must be conducted across countries. Additionally, policies and procedures for providing services to young offenders with mental illness must be improved, particularly in the areas of custodial and community mental health treatment.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".