Risk assessment matters, but only when implemented well: A multisite study in juvenile probation.
Bibliographic record
Abstract
There is a strong movement toward juvenile justice agencies' use of risk assessment and risk-need-responsivity approaches to improve case management decisions for young offenders. However, little is known about whether adoption of risk assessment actually effectuates any changes in the way young offenders are handled. This was a multisite study of the impact on case processing of implementation of the Structured Assessment of Violence Risk in Youth (SAVRY) or Youth Level of Service/Case Management Inventory in 6 juvenile probation offices using a prepost design and 1,694 propensity score-matched young offenders. Consistent with the risk principle, there were significant changes to at least some areas of case processing in all but 1 site, most notably with respect to decreases in the amount of supervision youth received and in rates of out-of-home placement. The nature and extent of the impact varied as a function of sites' characteristics and implementation quality, not as a function of the risk assessment used. No increases in recidivism were observed in any site, and there was a significant reduction in recidivism in 1 site. The key benefits of implementation of valid risk assessment and case management procedures were improved resource allocation and fewer instances of inappropriate interference in youths' lives without an apparent increased risk to public safety. (PsycINFO Database Record
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".