Association between traumatic brain injury and incarceration: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: There is recent evidence to suggest that sustaining a traumatic brain injury (TBI) increases risk of criminal justice system involvement, including incarceration. The objective of this study was to explore the association between TBI and risk of incarceration among men and women in Ontario. METHODS: We identified a cohort of 1.418 million young adults (aged 18-28 yr) on July 1, 1997, living in Ontario, Canada, from administrative health records; they were followed to Dec. 31, 2011. History of TBI was obtained from emergency and hospital records, and incarceration history was obtained from the Correctional Service of Canada records. We estimated the hazard of incarceration using Cox proportional hazard models, adjusting for relevant sociodemographic characteristics and medical history. RESULTS: There were 3531 incarcerations over 18 297 508 person-years of follow-up. The incidence of incarceration was higher among participants with prior TBI compared with those without a prior TBI. In fully adjusted models, men and women who had sustained a TBI were about 2.5 times more likely to be incarcerated than men and women who had not sustained a TBI. INTERPRETATION: Traumatic brain injury was associated with an increased risk of incarceration among men and women in Ontario. Our research highlights the importance of designing primary, secondary and tertiary prevention strategies to mitigate risk of TBI and incarceration in the population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".