The impact of mental health services on arrests of offenders with a serious mental illness.
Bibliographic record
Abstract
This study examines the impact of mental health services on arrests of offenders with a serious mental Illness (SMI) by assessing changes in associations between receipt of outpatient and emergency room/inpatient services and arrests one, two, and three quarters later. A variety of data sets were used for identifying 3,769 offenders who were in the Pinellas County Florida jail between 7/1/2003 and 6/30/ 2004, and 7,755 offenders who were in the Harris County Texas jail between 10/1/2005 and 9/30/2006. Arrests, out-patient and emergency room/inpatient services were assigned to one of 16 ninety-day periods between 7/1/2002 and 6/10/2006 in Pinellas County and one of 12 such periods between 10/1/2004 and 9/15/2007 in Harris County. Generalized estimating equations were used. Covariates were age, gender, race, diagnosis, and homelessness. The results were also adjusted for exposure to arrests. In Pinellas County, outpatient services significantly reduced the risks of arrests 1 quarter later by 17% (odds ratio [OR] = 0.83, 95% confidence interval [CI]: 0.78-0.87, p < .001), two quarters later by 11% (OR = 0.89, 95% CI: 0.84-0.94, p < .001), and three quarters later by 9% (OR = 0.91, 95% CI: 0.86-0.96, p = .001). In Harris County, these services reduced the risk of arrest 1 quarter later by 5% (OR = 0.95, 95% CI: 0.91-0.99, p = .028), but not two and three quarters later. In Pinellas County, ER/inpatient services increased the risk of arrests by 22% (OR = 1.23, 95% CI: 1.15-1.30, p < .001), 8% (OR = 1.08, 95% CI: 1.02-1.15, p = .010) and 11% (OR = 1.11, 95% CI: 1.02-1.16, p = .001) one, two, and three quarters later. In Harris County, these services increased the risk of arrest only 1 quarter later (OR = 1.16, 95% CI: 1.11-1.22, p < .001). Results suggest that service receipt and its timing may have had some impact on the arrests of adults with a SMI and criminal justice involvement.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".