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Record W4211049265 · doi:10.1089/jchc.20.05.0043

Understanding the Policy Landscape Surrounding Medical Assistance in Dying in Canada's Federal Prison System

2022· review· en· W4211049265 on OpenAlexaffabout
Peter Driftmier, Jessica Shaw

Bibliographic record

VenueJournal of Correctional Health Care · 2022
Typereview
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPrisonCommissionMedicineGuidelineNursingPatient rightsHealth careCriminologyLawPolitical sciencePsychology

Abstract

fetched live from OpenAlex

Medical assistance in dying (MAiD) has been legal in Canada since 2016, and the implementation of MAiD for people who are incarcerated has raised ethical and procedural concerns. In this article, we review the current Correctional Service Canada guideline on MAiD alongside a joint report by the Office of the Correctional Investigator (OCI) and the Canadian Human Rights Commission (CHRC) on aging and dying in prison. We echo concerns raised by the OCI and the CHRC about the limits of adequate end-of-life care currently provided to those in custody and offer our analysis of the procedural guideline for MAiD in prison, which we argue to be inadequate in support for patient-centered care and equality in access to health care.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.785
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.269
GPT teacher head0.470
Teacher spread0.201 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations4
Published2022
Admission routes2
Has abstractyes

Explore more

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