High-frequency use of corrections, health, and social services, and association with mental illness and substance use
Bibliographic record
Abstract
BACKGROUND: A subgroup of individuals becomes entrenched in a "revolving door" involving corrections, health, and social welfare services. Little research has investigated the numbers of people that are in frequent contact with multiple public agencies, the costs associated with these encounters, or the characteristics of the people concerned. The present study used linked administrative data to examine offenders who were also very frequent users of health and social services. We investigated the magnitude and distribution of costs attributable to different categories of service for those in the top 10 % of sentences to either community or custodial settings. We hypothesized that the members of these subgroups would be significantly more likely to have substance use and other mental disorders than other members of the offender population. METHODS: Data were linked across agencies responsible for services to the entire population of British Columbia spanning justice, health, and income assistance. Individuals were eligible for inclusion in the study if they were sentenced at least once in the Vancouver Provincial Court between 2003 and 2012. We examined the subset of participants who fell within the top 10 % of sentences and at least two of the following service categories: community physician services; hospital days; pharmaceutical costs; or income assistance between 2007 and 2012. We examined two groups of offenders separately (those in the top ten percent sentenced to community supervision or to custody) due to differences in time at risk and availability to receive community-based services. RESULTS: From more than 14,000 offenders sentenced in Vancouver's Downtown Eastside, very High Frequency service users associated with community (n = 216) and custody (n = 107) sentences incurred average attributable public service costs of $168,000 and $247,000 respectively over a 5-year period of observation. Health-related costs for both groups were over $80,000 per person, primarily associated with hospital admissions. Across both groups, 99 % had been diagnosed with at least one mental disorder and over 80 % had co-occurring substance use and another mental disorder. CONCLUSIONS: A subset of offenders with concurrent psychiatric disorders receives extremely high levels of service from health, social welfare, and justice sectors in close temporal succession. Members of this subpopulation require targeted supports in order to produce positive outcomes and prevent the perpetuation of a costly and ineffective revolving door.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".