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Record W4361000335 · doi:10.1097/jfn.0000000000000434

Canada's Aging Federal Prison Population

2023· article· en· W4361000335 on OpenAlexaffabout
Jim A. Johansson, Dave Holmes, Étienne Paradis-Gagné

Bibliographic record

VenueJournal of Forensic Nursing · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsUniversité de MontréalUniversity of Ottawa
Fundersnot available
KeywordsPrisonPopulationPopulation ageingHealth careForensic nursingGerontologyMedicineMental healthPsychologyPolitical scienceCriminologyPoison controlPsychiatryEnvironmental healthLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.952
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.327
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes2
Has abstractyes

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