Traumatic brain injury in criminal justice systems: a systematic literature review
Bibliographic record
Abstract
Traumatic brain injury (TBI) is common among justice-involved persons, creating substantial health and economic burdens owing to its association with a range of adverse psychosocial outcomes. No study to date has synthesised extant knowledge about TBI across the whole criminal justice pathway. We aimed to conduct a systematic review of the literature on TBI across this pathway, from arrest through to release from custody. Following PRISMA guidelines, five key electronic databases (PubMed, PsychInfo, Medline, Embase, Cinahl), Proquest Dissertations & Theses Global, Cochrane Database of Systematic Reviews, Web of Science, and grey literature were searched up to May 2023. Fifty-six reports met inclusion criteria. Three more reports were added after review. TBI prevalence rates ranged from 5.65% to 100% with higher rates among persons experiencing federal incarceration, justice-involved adolescents, and justice-involved veterans in the US. Severity of TBI was mostly mild. Studies reported positive associations between TBI and many psychosocial outcomes including violence, incarceration rates, cognitive impairment, mood disorders, psychosis, substance use disorders, and socioeconomic deprivation. Other adverse outcomes included reduced participation in educational activities and increased utilisation of mental health services. More research is needed to establish the true prevalence of TBI in criminal justice systems and the relationship between TBI and psychosocial as well as criminogenic outcomes.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.018 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".