Supervision trajectories of male juvenile offenders: growth mixture modeling on SAVRY risk assessments
Bibliographic record
Abstract
BACKGROUND: Structured risk/need assessment tools are increasingly used to orientate risk reduction strategies with juvenile offenders. The assumption is that the risk/need items on these tools are sufficiently sensitive to measure changes in the individual, family and/or contextual characteristics of juvenile offenders. However, there is very little research demonstrating the capacity of these tools to measure changes in juvenile offenders. Congruent with the developmental and life-course criminology theories (DLC) the objective of this study is to explore the existence of heterogeneous trajectories of juvenile offenders across the juvenile justice system as measured through five empirical risk/need areas based on the Structured Assessment of Violence Risk in Youth (SAVRY), one of the most widely applied risk assessment tools for juveniles. METHODS: This longitudinal study included 5205 male juvenile offenders who transitioned through the Catalan juvenile justice system between 2006 and 2014. During intervention they received at least two, and a maximum of seven, consecutive SAVRY risk/need assessments over an 18-month period. The heterogeneity of latent class trajectories was explored through growth mixture modeling (GMM). The trajectory class membership was linked to covariates through multinomial logistic regression analyses. RESULTS: Through GMM three to four heterogeneous trajectories, with high quality of separation, were identified in each of the risk/need areas. The trajectories with low risk/needs (45-77% of the sample) remained low and presented a very limited increase in risk/needs during the 18-month period. The high risk/need trajectories (20-37% of the sample) showed a limited decrease or no change. Between 5 and 13% of the sample had large reductions in their risk/needs levels, and approximately 5% showed a large increase in risk/needs. CONCLUSIONS: In line with the DLC theories this study shows that trajectories on criminogenic risk/needs can be heterogeneous and indicate distinct rates of change over time. The results of this study also may suggest a limited sensibility to measure change over time of SAVRY's risk and protective items. Suggestions to improve the sensitivity of measuring change over time, such as shorter time frames or future-oriented time frames for the scoring of the items, are offered.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".