An Examination of Criminogenic Needs, Mental Health Concerns, and Recidivism in a Sample of Violent Young Offenders: Implications for Risk, Need, and Responsivity
Bibliographic record
Abstract
Young offender populations typically display high rates of substance use pathology and mental health concerns, however, little is known regarding how these factors relate to dynamic risk factors for reoffending (criminogenic needs) among young offenders. The present study investigated a Canadian sample of 186 youth charged with serious/violent offenses on measures of psychopathology, substance abuse, risk, and recidivism. Significant relationships were found between measures of substance abuse with most indices of the Youth Level of Service/Case Management Inventory (YLS/CMI), a validated risk assessment tool designed to assess criminogenic risk and need. Furthermore, measures of substance abuse predicted general, violent, and nonviolent recidivism for both youth and adult outcomes to varying degrees. Youth with disruptive behavior disorders, comorbid substance use concerns with another disorder (dual diagnosis), or with two or more disorders evidenced more serious criminogenic need profiles, whereas mood, anxiety, and cognitive disorders were unrelated to criminogenic risk. With the exception of conduct disorder and substance use pathology, mental health concerns tended not to be related to recidivism. The implications of these findings in terms of assessing risk and providing treatment services for young offenders is discussed in relation to the risk-need responsivity (RNR) model of effective correctional intervention.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".