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Record W4321614159 · doi:10.1037/pro0000500

Medical assistance in dying (MAiD): Ethical considerations for psychologists.

2023· article· en· W4321614159 on OpenAlex
Gerald P. Koocher, G. Andrew H. Benjamin, Jonathan Bolton, Thomas G. Plante

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueProfessional Psychology Research and Practice · 2023
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyEngineering ethicsCriminologySociologyPsychoanalysisPolitical scienceEngineering

Abstract

fetched live from OpenAlex

Significant ethical challenges arise when mental health practitioners care for patients who seek to accelerate their own dying for rational medically valid reasons. Current and proposed laws provide for medical assistance in dying (MAiD) in several U.S. jurisdictions, all of Canada, and several other nations. Differing provisions of these laws complicate their utility for some patients who seek aid in dying. Some extant laws include roles that mental health professionals might play in assessing patients’ competence or capacity to consent, mental illness, or other cognitive and behavioral factors. Practitioners who choose to accept roles in the MAiD process must consider and resolve a number of ethical challenges including potential conflicts between and among laws, ethical standards, third-party requests, personal values, and patients’ wishes. These include becoming aware of patients who may wish to act independently to end their lives when MAiD laws might otherwise exclude them. Examples from actual cases and the resultant discussion will form a basis for exploration of the ethical and legal complexities confronted when psychologists become engaged in the process either intentionally or incidentally. The lead article (Koocher) is not intended to comprehensively address MAiD in all of its complexity but rather to trigger a thoughtful discussion among the accompanying commentaries.

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.

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)0.008
metaresearch head score (Gemma)0.057
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.720
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.057
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.680
GPT teacher head0.705
Teacher spread0.024 · 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