Overall and substance use-specific healthcare utilization among individuals with and without criminal justice involvement in Ontario, Canada
Bibliographic record
Abstract
PURPOSE: Correctional populations have higher rates of substance use disorders and related healthcare visits relative to the general population. However, limited evidence on substance use-related healthcare visits exists among this population. Using population data for Ontario, Canada, this study aims to examine overall and substance use-specific healthcare visits for individuals with and without known provincial criminal justice system involvement (CJI versus non-CJI, respectively). DESIGN/METHODOLOGY/APPROACH: This retrospective study compared overall and substance use-related healthcare visits between April 1, 2015 and March 31, 2020 among provincially-incarcerated individuals (CJI group) versus those without criminal justice involvement (non-CJI group). Both groups were identified through available health administrative data and were individually matched by age, sex and material deprivation. FINDINGS: The authors identified and matched 208,188 individuals (59.9% male) with and without CJI and a healthcare visit. Compared to the non-CJI group, those with CJI had approximately 20 times the rate of healthcare visits for alcohol use, drug use and illicit drug-related overdoses. Among those with CJI, females had a higher prevalence of overall healthcare visits, whereas males had a higher prevalence of substance use-specific visits. RESEARCH LIMITATIONS/IMPLICATIONS: Findings highlight the high number of healthcare visits for substance use-related needs among individuals with CJI in Ontario. These results can inform efforts to enhance correctional release planning, improve access to community-based treatment and strengthen substance use prevention and treatment interventions for this high-risk population. PRACTICAL IMPLICATIONS: Results can inform efforts to enhance correctional release planning, improve access to community-based treatment, and strengthen substance use prevention and treatment interventions for this high-risk population. ORIGINALITY/VALUE: To the best of the authors' knowledge, this study is the first in Canada to draw on population-level administrative health data to identify and match a large sample of individuals with and without CJI and examine substance use-specific healthcare utilization, longitudinally.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".