Canada's Aging Federal Prison Population
Bibliographic record
Abstract
ABSTRACT: The Canadian federal prison population is increasingly aging within institutions that were never intended or designed to meet the complex medical and mental health needs of older incarcerated persons. Increasing numbers of incarcerated persons are "aging in place," and many are dying within federal correctional institutions. Persons convicted of sexual offenses comprise a large-and growing-proportion of this aging population. The Correctional Investigator Canada has recently called for an expansion of access to compassionate release for the aging federal prison population, yet little progress has been made. In this article, we explore the significant challenges faced by the aging population in federal institutions, including insufficient access to appropriate care, challenges in application for compassionate release, and how questions of risk may affect the potential for community transfer. Questions of risk overshadow decisions on early release of incarcerated persons, especially those convicted of sexual offenses. Nurses play a central role in the provision of care to aging incarcerated persons and in advocacy for better access to services when a patient's needs cannot be met within the institution. This article presents a call to action for forensic nurses in Canada (and beyond) to advocate for both improved services within federal correctional institutions and for expedited access to compassionate release of aging incarcerated persons, especially those nearing end of life. The significant disparity in access to health care for aging incarcerated persons compared with their nonincarcerated counterparts represents a significant concern.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".