Adverse Childhood Experiences and Justice System Contact: A Systematic Review
Bibliographic record
Abstract
CONTEXT: Given the wide-ranging health impacts of justice system involvement, we examined evidence for the association between adverse childhood experiences (ACEs) and justice system contact in the United States. OBJECTIVE: To synthesize epidemiological evidence for the association between ACEs and justice system contact. DATA SOURCES: We searched 5 databases for studies conducted through January 2020. The search term used for each database was as follows: ("aces" OR "childhood adversities") AND ("delinquency" OR "crime" OR "juvenile" OR criminal* OR offend*). STUDY SELECTION: We included all observational studies assessing the association between ACEs and justice system contact conducted in the United States. DATA EXTRACTION: Data extracted from each eligible study included information about the study design, study population, sample size, exposure and outcome measures, and key findings. Study quality was assessed by using the Newcastle-Ottawa Scale for nonrandomized trials. RESULTS: In total, 10 of 11 studies reviewed were conducted in juvenile population groups. Elevated ACE scores were associated with increased risk of juvenile justice system contact. Estimates of the adjusted odds ratio of justice system contact per 1-point increase in ACE score ranged from 0.91 to 1.68. Results were consistent across multiple types of justice system contact and across geographic regions. LIMITATIONS: Most studies reviewed were conducted in juvenile justice-involved populations with follow-up limited to adolescence or early adulthood. CONCLUSIONS: ACEs are positively associated with juvenile justice system contact in a dose-response fashion. ACE prevention programs may help reduce juvenile justice system contacts and improve child and adolescent health.
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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 | 0.010 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".