Patterns of Change in Dynamic Risk Factors over Time in Youth Offenders
Bibliographic record
Abstract
Risk assessments that include dynamic risk factors are increasingly being utilized within the youth justice system to predict a young person’s likelihood to reoffend, to assist with case management, and to better inform intervention services. However, most studies to date have relied solely on single-wave cross-sectional research designs that essentially treat dynamic risk factors as static. Thus, it is unclear whether and how putative dynamic risk factors change over time, a question that has significant implications for assessment and case management policy and practice. Using a widely used and validated risk assessment and case management instrument (the Youth Level of Service/Case Management Inventory), the purpose of the present study was to examine whether the dynamic risk factors outlined in the Risk-Need-Responsivity (RNR) model do in fact change over time and, if so, to investigate the effect of youth-specific predictors on these changes. Two hundred youth offenders were tracked from their first risk assessment conducted at probation to their transition out of the youth justice system. Results from generalized linear mixed modelling (GLMM) and latent class growth modelling (LCGM) analyses indicated that most dynamic risk domain scores increased over time, but that there was significant individual variation among youth at initial status and in the rate of change. Even when controlling for youth-specific factors, youth who were lower risk at the time of initial assessment increased in risk at a greater rate than higher-risk youth. Results have implications for the RNR framework, for improving the accuracy of risk assessments, and for informing treatment implementation.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".