Complex Trauma and Criminogenic Needs in a Youth Justice Sample: A Gender-Informed Latent Profile Analysis
Bibliographic record
Abstract
How complex trauma features and criminogenic needs co-vary within youth justice populations requires examination. This study applies latent profile analysis to a sample of 311 justice-involved Canadian youth (211 male, 100 female) to identify if unique profiles of youth would emerge delineated by different combinations of comorbid needs pulled from complex trauma and personality/social learning models. Two similar profiles emerged for males and females alike: a complex trauma with criminogenic needs profile (70% of females, 58.8% of males) and a low overall needs profile (30% of females, 41.2% of males). Surprisingly, the Youth-Level Service/Case Management Inventory predicted recidivism well among the complex trauma/criminogenic need female cases (AUC = .71), but poorly among the complex trauma/criminogenic need male cases (AUC = .59). Trauma-informed approaches that target criminogenic needs in both genders is a clear implication of the findings.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".