Moving towards a trauma-informed Canadian correctional system
Bibliographic record
Abstract
Marginalized and stigmatized peoples characterized by social, economic, and psychological disadvantage are overrepresented within the prison population. Many offenders have various, complex, and inter-related needs that often include a combination of substance abuse, mental health issues, childhood trauma or Adverse Childhood Experiences (ACEs), and Post Traumatic Stress Disorder (PTSD).\nThese complex populations have historically been managed through the overuse of segregation placements. However, courts in Ontario and British Columbia (BC) recently found segregation to be unconstitutional, prompting the federal government to abolish segregation in Canadian federal institutions and create Structured Intervention Units (SIUs). Yet there are concerns that SIUs do not go far enough in protecting vulnerable in custody offenders and that more effort is needed to prevent the need for segregation through a better understanding of trauma and mental health needs of inmates.\nThis paper will provide recommendations for prevention based interventions and assessments directed towards offenders with complex needs, including training for all frontline staff in trauma-informed practice, present-focused trauma programming, and utilizing actuarial tools to screen offenders at intake to assist in preventing SIU placements. These recommendations will provide agencies like the Correctional Service of Canada with clear strategies to implement in moving towards a trauma-informed system.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".