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Record W4390746141 · doi:10.15388/teise.2023.129.7

Canadian Codes of Medical Ethics as a Source of Law

2024· article· en· W4390746141 on OpenAlexfundaboutno aff
Anatoliy A. Lytvynenko, Tetiana V. Petlina

Bibliographic record

VenueTeisė · 2024
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsnot available
FundersUniversity of Alberta
KeywordsNormativeLegislatureMedical ethicsLawEthical codePolitical scienceLegal ethicsMedical classificationEngineering ethicsPsychologyMedicineEngineeringNursing

Abstract

fetched live from OpenAlex

The article discusses the codes of medical ethics which are adopted in Canada. These acts are not ‘classical’ normative-legal acts, since they are adopted not by legislative bodies, but rather by medical associations, and are aimed at providing rules on the professional conduct of physicians and auxiliary medical personnel; occasionally, they also feature the rules concerning the rights and duties of patients. The article’s sources include the codes of medical ethics adopted in Canada, Canadian court judgments, and academic literature.

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)0.049
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 score0.137
Threshold uncertainty score0.993

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.049
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0070.011
Science and technology studies0.0150.013
Scholarly communication0.0150.003
Open science0.0020.004
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0090.002

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 head0.181
GPT teacher head0.572
Teacher spread0.391 · 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
Published2024
Admission routes2
Has abstractyes

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