Short and long-term prediction of recidivism using the youth level of service/case management inventory in a sample of serious young offenders.
Bibliographic record
Abstract
The present investigation examined the predictive accuracy of the Youth Level of Service/Case Management Inventory (YLS/CMI) for youth and adult recidivism in a Canadian sample of 167 youths (93 males, 74 females) charged with serious offenses who received psychological services from a community mental health outpatient clinic. Youths were followed for an average of 7 years in the community, and predictive accuracy was examined for several recidivism outcomes as a function of gender, ethnicity, and developmental age group. YLS/CMI total scores significantly predicted all recidivism categories in the overall sample (area under the curve values ranged from 0.66 to 0.77) although the instrument as a whole, and its criminogenic needs, demonstrated somewhat stronger and more consistent predictive accuracy for youth outcomes. The YLS/CMI also demonstrated significant predictive accuracy within demographic subgroups. The implications of these findings are discussed in terms of the use of risk-need assessment tools in providing clinical assessment, treatment, and case management services to diverse young offender groups.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".