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Record W4391480160 · doi:10.1017/s1041610223002284

Legislation of euthanasia in Portugal: The psychiatrist’s role

2023· article· en· W4391480160 on OpenAlexaboutno aff
Lia Fernandes, Rui Barranha, Ana R. Ferreira

Bibliographic record

VenueInternational Psychogeriatrics · 2023
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsnot available
Fundersnot available
KeywordsLegislationParliamentPortugueseContext (archaeology)Inclusion (mineral)Political scienceMedicineHealth careLawPsychologyPoliticsHistory

Abstract

fetched live from OpenAlex

The Netherlands (2001) and Belgium (2002) were pioneer countries in euthanasia legislation, and it has been progressively implemented in Luxembourg (2009), Colombia (2015), Canada (2016), the state of Victoria in Australia (2017), and New Zealand (2021). After these, the most recent approvals of euthanasia-related legislation were Spain's Organic Law (2021), and recently, in December 2022, euthanasia and physician-assisted suicide were approved by the Portuguese parliament, following four years of discussion.In this context, an increasing number of countries are currently regulating or considering the inclusion of euthanasia as a healthcare service, which places an urgent need to define the health professionals' and particularly psychiatrists' roles and participation in this process. Currently, these assessments are limited by the lack of scientific precision in the methods used to determine patients' decision-making capacity, conditioning this process to psychiatrists' personal beliefs.This presentation will provide a comprehensive overview of Portuguese legislation, highlighting the psychiatrists' involvement.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0050.009
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.105
GPT teacher head0.438
Teacher spread0.334 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2023
Admission routes1
Has abstractyes

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