Adverse Childhood Experiences and the Risk of Criminal Justice Involvement and Victimization Among Homeless Adults With Mental Illness
Bibliographic record
Abstract
OBJECTIVE: Exposure to adverse childhood experiences (ACEs) is highly prevalent among homeless individuals and is associated with negative consequences during homelessness. This study examined the effect of ACEs on the risk of criminal justice involvement and victimization among homeless individuals with mental illness. METHODS: The study used baseline data from a demonstration project (At Home/Chez Soi) that provided Housing First and recovery-oriented services to homeless adults with mental illness. The sample was recruited from five Canadian cities and included participants who provided valid responses on an ACEs questionnaire (N=1,888). RESULTS: Fifty percent reported more than four types of ACE, 19% reported three or four types, 19% reported one or two, and 12% reported none. Rates of criminal justice involvement and victimization were significantly higher among those with a history of ACEs. For victimization, the association was significant for all ten types of ACE, and for justice involvement, it was significant for seven types. Logistic regression models indicated that the effect of cumulative childhood adversity on the two outcomes was significant regardless of sociodemographic factors, duration of homelessness, and psychiatric diagnosis, with one exception: the relationship between cumulative childhood adversity and criminal justice involvement did not remain significant when the analysis controlled for a diagnosis of posttraumatic stress disorder and substance dependence. CONCLUSIONS: Findings support the need for early interventions for at-risk youths and trauma-informed practice and violence prevention policies that specifically target homeless populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".