Childhood Maltreatment and the Risk for Criminal Justice Involvement and Victimization Among Homeless Individuals: A Systematic Review
Bibliographic record
Abstract
Homeless individuals are at higher risk of criminal justice involvement (CJI) and victimization compared to their housed counterparts. Exposure to childhood maltreatment (CM; e.g., abuse, neglect) is one of the most significant predictors of CJI and victimization among homeless populations. The aim of this systematic review was to synthesize current knowledge regarding the relationship between CM and CJI and victimization among homeless individuals. Guided by the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methods, a systematic search was performed using PsycINFO, MEDLINE, Embase, Web of Science, and the Cumulative Index to Nursing and Allied Health Literature for published studies investigating the relationship between CM and CJI and victimization among homeless samples. We identified 20 studies that met the inclusion criteria. Findings showed that across the majority of studies, CM, and in particular childhood physical (CPA) and sexual (CSA) abuse, is associated with increased risk of both CJI and victimization, regardless of various important factors (e.g., sociodemographic characteristics, psychiatric disorders, substance use). These findings support the need for prevention and treatment for "families at risk" (i.e., for intimate partner violence, child abuse and neglect) and also document the need for trauma-informed approaches within services for homeless individuals. Future research should focus on prospective designs that examine victimization and CJI in the same samples.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".