Improving Health and Healthcare Access for People who Experience Imprisonment in Ontario
Bibliographic record
Abstract
People who experience imprisonment have worse health status than other Ontarians -about 40% lack access to primary care in the community, and the period after release from prison is associated with high risks of adverse health outcomes.Population-based correctional and health administrative data suggest that access to quality healthcare in prison and in the community needs to improve if we are to improve population health and deliver on healthcare obligations to people experiencing imprisonment. ContextOn an average day, 38,786 people are detained or incarcerated in prisons 1 across Canada (Malakieh 2019).The number of people who experience imprisonment per year is much larger, but these data are not collected at the national level (Kouyoumdjian and McIsaac 2017).In Ontario provincial prisons alone, there are nearly 7,500 people in custody on an average day (Malakieh 2019), and about 40,000 people are imprisoned annually (Expert Advisory Committee on Health Care Transformation in Corrections 2018).The state assumes legal responsibilities for people in its prisons.These obligations are enshrined in provincial and federal legislation and internationally in the United Nations' Nelson Mandela Rules.The Mandela Rules state that "prisoners should enjoy the same standards of health-care that are available in the community … and should have access to necessary health-care services … without discrimination on the grounds of their legal status" and that prison healthcare services should be organized "in a way that ensures continuity of treatment and care" and be delivered by "qualified personnel acting in full clinical independence" (UN General Assembly 2015).Disparities in health and access to healthcare between people who experience imprisonment and the rest of the population suggest an imperative to improve care.Data on health and healthcare among people experiencing imprisonment in Ontario are not routinely collected, which precludes routine population health assessment, surveillance and healthcare quality assurance.We merged population-based correctional and health administrative data to examine health and healthcare utilization among 48,861 people who experienced imprisonment in provincial prisons in Ontario in 2010.In this article, we highlight three of our findings and discuss their implications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".