Beyond recidivism: changes in health and social service involvement following exposure to drug treatment court
Bibliographic record
Abstract
BACKGROUND: The majority of Drug Treatment Court (DTC) research has examined the impact of DTCs on criminal recidivism. Comparatively little research has addressed the association between DTC participation and engagement with community-based health and social services. The present study investigated changes in participant involvement with outpatient healthcare and income assistance within a DTC cohort. We hypothesized that involvement with community-based (outpatient) health and social services would increase post-DTC participation, and that service levels would be higher among program graduates and offenders with histories of co-occurring mental and substance use disorders. METHODS: Participants were 631 offenders at the DTC in Vancouver, Canada (DTCV). Administrative data representing hospital, outpatient medical care, and income assistance were examined one-year pre/post program to assess differences over time. Generalized estimating equations were used to investigate the association between changes in service use and program involvement. We also examined the relationship between level of service use and offender characteristics. RESULTS: Members of the cohort were disproportionately Aboriginal (33 %), had been sentenced 2.7 times in the 2 years preceding their index offence, and 50 % had been diagnosed with a non substance-related mental disorder in the five years preceding the index offence. The mean number of outpatient services post DTCV was 51, and the mean amount of social assistance paid was $5,897. Outpatient service use increased following exposure to DTCV (Adjusted Rate Ratio (ARR) = 1.45) and was significantly higher among women (ARR = 1.47), program graduation (ARR = 1.23), and those previously diagnosed with concurrent substance use and mental disorders (ARR = 4.92). Overall, hospital admissions did not increase post-program, although rates were significantly higher among women (ARR = 1.76) and those with concurrent disorders (ARR = 2.71). Income assistance increased significantly post program (ARR = 1.16), and was significantly higher among women (ARR = 1.03), and those diagnosed with substance use disorders (ARR = 1.42) and concurrent disorders (ARR = 1.72). CONCLUSIONS: These findings suggest that the DTCV was a catalyst for increased participant engagement with community health and social supports, and that rates of service use were consistently higher among women and individuals with concurrent disorders. Research is needed to investigate the potential link between health and social support and reductions in recidivism associated with DTCs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".