Exploring the Gendered Nature and Prevalence of Adverse Childhood Experiences, Developmental Trauma, and Complex Trauma in a Sample of Justice-Involved Youth
Bibliographic record
Abstract
In Canada, under Section 34 of the Youth Criminal Justice Act (YCJA), courts can order psychological assessments for high-risk youth to aid in informed sentencing and rehabilitation decisions. Clinical files prepared by a youth justice clinic to inform Section 34 reports for 1205 youth charged with serious offences will be used to examine the prevalence of adverse childhood experiences (ACEs), developmental trauma, and complex trauma. Additionally, we will investigate gender differences in the presence of adversity and trauma within the sample. We hypothesize that the accumulation of ACEs, developmental trauma, and complex trauma will increase the likelihood of youth criminal behavior. This hypothesis aligns with previous literature, which has shown that ACEs may not fully capture the extent of adversities experienced by youth, implicating developmental and complex trauma in crime trajectories as well. Our preliminary analysis of a subset of the sample reveals high levels of ACEs, with parental divorce being most frequently reported. While females generally reported higher victimization rates, significant victimization is observed across genders. Currently, manual coding of clinical notes used to inform Section 34 assessments is underway, spearheaded by a collaborative effort between students from Carleton University and the University of Toronto. We anticipate that our findings will illuminate the co-occurrence of ACEs, developmental trauma, and complex trauma within this sample, shedding light on their contributions to criminal behavior. Moreover, we hope that our study will offer insights into how these adversities can be mitigated by identifying individual strengths that increase the likelihood of crime desistance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".