Risk of violence by inmates with childhood trauma and mental health needs.
Bibliographic record
Abstract
Inmates who experienced childhood trauma have higher rates of institutional violence. However, the potential intermediate roles of co-occurring mental health and substance use needs and early justice involvement have not previously been considered. The current study examined the relationships between trauma, mental health, substance abuse, youth criminal charges, and institutional violence during the first 180 days of incarceration. As secondary aims, we explored whether these associations differed by sex or differed for inmates of Aboriginal ethnicity. Secondary data from prison records for all 5,154 inmates admitted to a federal prison during 2011 were collected. Path analysis was used to estimate the direct and indirect associations between trauma and institutional violence. Approximately 45% of inmates reported childhood trauma, which was associated with a higher prevalence of co-occurring mental health and substance abuse needs, and youth criminal charges. Although mental health, substance abuse, and youth criminal charges interacted with one another in predicting violence, their associations were similar for those with and without histories of trauma. A direct association between trauma and institutional incidents remained (Relative Risk [RR] = 1.38, 95% CI [1.07, 1.78]) after accounting for indirect associations through these co-occurring risk factors. There was insufficient evidence to suggest that these associations differed between men and women or between Aboriginal and non-Aboriginal inmates. Given the high co-occurrence of multiple health and behavioral risk factors for inmates with traumatic histories, clarifying which factors are causally associated and reversible is needed to inform effective trauma informed care.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".