Towards clinically meaningful subtyping of youth with violent behavior: application of latent profile analysis to a risk-strengths based risk assessment model*
Bibliographic record
Abstract
The ultimate goal of forensic interventions is reducing risk level by targeting criminogenic needs. Person-centered approaches are used to identify subgroups with similar patterns of needs, informing treatment targeting differential criminogenic areas. In line with risk and strengths-based theories on offender rehabilitation, this paper identified subgroups based on risk and protective factors. In 297 justice involved youth with a history of violence, subgroups were identified using latent profile analysis on subscale ratings of the SAVRY and SAPROF-YV. For 216 youths these profiles were related to recidivism. Four latent profiles were identified varying in risk and protection level. These profiles showed strong concordance with structured professional judgement classifications and differentiating offending patterns were observed between subgroups. Results show how risk factors and protective factors tend to co-occur for subgroups of young individuals, which could facilitate allocation of intervention resources and inform better tailored case management strategies aimed at reducing risk factors and improving strengths to enhance resilience.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".