Trauma‐informed justice in child abuse cases: A literature review
Bibliographic record
Abstract
Abstract Child abuse is a pervasive form of gender‐based violence that inflicts lasting trauma on its victims, their families, and their communities. Participation in the criminal legal system can exacerbate this trauma, particularly for children and youth experiencing intersecting and systemic inequalities. Trauma‐informed practices have emerged as a framework for various systems to recognize the effects of trauma and adapt interactions to promote healing and reduce re‐traumatization. This raises an overarching question of what the elements of a trauma‐informed approach to child abuse in the criminal legal system are. This article synthesizes the state of the literature and current practices as they relate to trauma‐informed approaches for children and youth victimized by child abuse who are engaged with the criminal legal system, focusing on the Canadian legal and policy context. We summarize the impact of legal system involvement for these children and youth and explore how trauma‐informed approaches are conceptualized within the criminal legal system, including restorative and transformative justice approaches. We also identify core elements of trauma‐informed approaches to the legal process in child abuse cases and the associated policy and practice implications for the criminal legal system. Lastly, we identify gaps in the literature and outline future directions and recommendations for research to expand knowledge and reduce system‐induced trauma for these vulnerable young people.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".