Prevalence and Predictors of Reincarceration after Correctional Center Release: A Population-based Comparison of Individuals with and without Schizophrenia in Ontario, Canada: Prévalence et prédicteurs de la réincarcération après la libération d’un centre correctionnel : une comparaison dans la population-de personnes souffrant ou non de schizophrénie en Ontario, Canada
Bibliographic record
Abstract
OBJECTIVES: Individuals with schizophrenia are overrepresented in correctional facilities relative to their population-based prevalence. The purpose of this study was to determine the rate and predictors of reincarceration of individuals with schizophrenia after release from correctional facilities. METHODS: This was a retrospective cohort study that included all people released from Ontario's provincial correctional facilities from January 1 to December 31, 2010. Individuals with schizophrenia were identified using a population-based algorithm. The primary outcome was time to reincarceration. Covariates included sociodemographic characteristics (age, sex, neighborhood income quintile, urban/rural residence), health service utilization (primary care physician visits, psychiatrist visits, psychiatric and nonpsychiatric hospitalizations, emergency department visits), and other clinical comorbidity. Survival analysis was used to examine the association between schizophrenia and reincarceration. RESULTS: = 3,237 (7%) had a diagnosis of schizophrenia. Approximately 67.5% of these individuals were reincarcerated within 5 years following their first release in 2010, compared to 58.8% of individuals without schizophrenia. Individuals with schizophrenia were 40% (HR = 1.39, 95% CI, 1.33 to 1.45) more likely to be reincarcerated following release than the control group after adjusting for demographic characteristics. This association reduced to 8% (HR = 1.08, 95% CI,1.03 to 1.14) after adjusting for prior health service utilization, prior correctional involvement, and comorbidities. CONCLUSION: Individuals with schizophrenia were more likely to experience reincarceration after release from correctional facilities. This risk is partly explained by prior correctional involvement, health service utilization, and comorbidities. Future research should focus on risk factors predicting the higher reincarceration rate and interventions to reduce correctional 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".