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Record W3211232006 · doi:10.25259/ijpc_426_20

Regulating Death: A Brief History of Medical Assistance in Dying

2021· article· en· W3211232006 on OpenAlexaffabout

Bibliographic record

VenueIndian Journal of Palliative Care · 2021
Typearticle
Languageen
FieldMedicine
TopicEthics and Legal Issues in Pediatric Healthcare
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMoresOpposition (politics)Hippocratic OathMedical ethicsAssisted suicidePalliative careRight to dieTheme (computing)Mental illness

Abstract

fetched live from OpenAlex

Unique reports of suicide and euthanasia date back more than 2 millennia, reflecting evolving philosophies of death and dying as expressions of the mores dominating a given era. One longstanding theme in the history of decisions to die has been staunch opposition founded in religious claims that one’s body is a trust from the divine (and therefore not wholly in their ownership). The role of the physician has also been traditionally estranged from participation in such decisions, dating back to rudimentary conceptions of medical ethics in the Hippocratic notion primum non nocere (‘first, do no harm’). However, fundamental principles in the modern philosophy of medicine lend support to the idea that physicians can be justified in actions which cause some harm, in so far as they are acting to fulfil a greater ethical imperative. This brief historical review explores the inception of modern North American medical assistance in dying (MAiD) policy through a series of critical case studies in the unfolding of its practice. Medically assisted dying has presently been legalised in Canada and some United States jurisdictions, but with critical caveats surrounding circumstances of mature minors, advance directives and mental illness as participants’ sole underlying medical condition. While the modern regulations surrounding MAiD continue to take shape, the palliative care community is well-positioned to both guide and scrutinise the ethics of this practice.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score0.825

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.080
GPT teacher head0.393
Teacher spread0.313 · 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 designObservational
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

Citations9
Published2021
Admission routes2
Has abstractyes

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