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Record W3012784250 · doi:10.1177/0969733020906606

Human rights and nursing codes of ethics in Canada 1953–2017

2020· article· en· W3012784250 on OpenAlexaffabout
Dawn Tisdale, Paisly Symenuk

Bibliographic record

VenueNursing Ethics · 2020
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHuman rightsEthical codeNursing ethicsContext (archaeology)NursingPolitical scienceSociologyLawMedicineGeography

Abstract

fetched live from OpenAlex

Human rights are foundational to the health and well-being of all individuals and have remained a central tenet of nursing's ethical framework throughout history. The purpose of this study is to explore continuity and changes to human rights in nursing codes of ethics in the Canadian context. This study examines nursing codes of ethics between the years 1953 and 2017, which spans the very first code in Canada to the most recently adopted. The historical method is used to compare and contrast human rights language, positioning and descriptions between different code editions. The findings suggest there has been very little change in how human rights have been included within the Canadian nursing codes of ethics. Furthermore, we consider how changes within the nursing profession have influenced the authority of codes of ethics and their ability to support nurses in carrying out ethical obligations specific to human rights. Finally, the impacts and implications of these changes are discussed concerning the protection of human rights in today's healthcare landscape in Canada.

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)0.006
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.370
Threshold uncertainty score0.730

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.009
Science and technology studies0.0210.017
Scholarly communication0.0090.002
Open science0.0020.004
Research integrity0.0020.005
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 head0.382
GPT teacher head0.572
Teacher spread0.190 · 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 designQualitative
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

Citations13
Published2020
Admission routes2
Has abstractyes

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