Health Status of Females Who Experience Incarceration: A Population-Based Retrospective Cohort Study
Bibliographic record
Abstract
Background: People who experience incarceration have poor health across a variety of indicators, but we lack population-level data on the health of females in particular. We examined the health status of females released from provincial prison, and compared their data with data for males released from provincial prison and females in the general population in Ontario, Canada in 2010. Methods: We conducted a retrospective cohort study using linked correctional and health administrative data. We compared sociodemographic data, morbidity, mortality, and use of health care for (1) females released from provincial prison in 2010, (2) males released from provincial prison in 2010, and (3) age-matched females in the general population. Results: Females in the incarceration group ( N = 6,107) were more likely to have higher morbidity and specific psychiatric conditions compared with the male incarceration group ( N = 42,754) and the female general population group ( N = 24,428). Their mortality rate postrelease was several times higher than that for the female general population group. They used primary care more often than both comparator groups across all time periods, and they used emergency departments more often compared with the female general population group and in most periods postrelease compared with the male incarceration group. They also tended to have higher rates of medical-surgical and psychiatric hospitalization. Conclusion: Females who experience incarceration have worse health overall than males who experience incarceration and females in the general population. Efforts should be made to reform programs and policies in the criminal justice and health care systems to support and promote health for females who experience incarceration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".