Adverse Childhood Experiences (ACEs) and Recidivism in Justice-Impacted Youth: Considering Gender Differences and the Mediating Effects of Mental Health
Bibliographic record
Abstract
Young persons who have experienced trauma in their childhood are disproportionately represented in the legal system (Malvaso et al., 2022) and are more likely to recidivate compared to their peers without trauma histories (Yannon et al., 2024). This study assessed whether the predictive relationship between adverse childhood experiences (ACEs) and recidivism in justice-impacted youth was mediated by the presence of mental health diagnoses. The narrative clinical notes of clinicians, used to inform the forensic assessments of youth (N = 321) referred for a court-ordered mental health assessment, were retrospectively coded for mental health diagnoses and trauma histories. Higher ACEs scores did not predict recidivism; however, they did predict a higher number of mental health diagnoses in both girls and boys. This research affirms the need to implement trauma-responsive care which specifically targets the mental health needs of justice-impacted youth.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".