COVID-19 impacts on decarceration for Indigenous, Black, and other racialized people in Ontario, Canada: an interrupted time series study
Bibliographic record
Abstract
Background: The COVID-19 pandemic response in many jurisdictions included efforts to depopulate correctional facilities. In the context of the overrepresentation of Indigenous and Black people in Canadian correctional facilities, we aimed to assess COVID-19 impacts on decarceration by race and Indigenous identity in Ontario, Canada. Methods: We accessed correctional administrative data for all people incarcerated in provincial correctional facilities in Ontario, Canada between 2015 and 2022. We categorized people using self-reported data into one of five identity groups: Indigenous, non-Indigenous Black, non-Indigenous non-Black racialized, non-Indigenous white, or missing. We conducted interrupted time series analyses, treating COVID-19 as an event on April 1, 2020, for each of admissions, releases, number of people in custody, and person-time in custody. Findings: Of 148,937 people who experienced incarceration, 85.4% were male and 14.5% were female, the mean age was 35.2 years (SD 12.2), and 11.7% were Indigenous, 12.1% were non-Indigenous Black, 12.1% were non-Indigenous non-Black racialized, and 48.9% were non-Indigenous white. Decarceration in the spring of 2020 benefitted all four race/Indigenous identity groups, with significant decreases in all four decarceration indicators for all groups. There was a significant interaction between COVID-19 decarceration and race/Indigenous identity group for the number of people in custody (p < 0.0001) and person-time in custody (p = 0.042), with decarceration disproportionately benefitting non-Indigenous white people. Compared with the period prior to April 2020, the relative rates of being in custody and of person-time in custody, respectively, were 0.70 (95% CI 0.68-0.73) and 0.73 (95% CI 0.70-0.76) for non-Indigenous white people, lower than those for Indigenous people: 0.76 (95% CI 0.72-0.81) and 0.82 (95% CI 0.76-0.88), non-Indigenous Black people: 0.76 (95% CI 0.74-0.78) and 0.79 (95% CI 0.76-0.81), and non-Indigenous non-Black racialized people: 0.76 (95% CI 0.73-0.79) and 0.79 (95% CI 0.76-0.83). Interpretation: Decarceration in Ontario in 2020 was inequitable, exacerbating the disproportionate exposure of people who are Indigenous and Black to time in custody and to the adverse health impacts associated with incarceration during the COVID-19 pandemic. These findings emphasize the need for targeted strategies to foster equitable health and justice outcomes, including during public health emergencies. Funding: Department of Family Medicine, McMaster University.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".