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Record W2018106779 · doi:10.12927/cjnl.2010.21941

Conscientious Objection: A Call to Nursing Leadership

2010· article· en· W2018106779 on OpenAlexaffvenueabout
Natalie Ford, Kimberly D. Fraser, Patrícia Marck

Bibliographic record

VenueNursing leadership · 2010
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsConscientious objectorConscienceNursingScope (computer science)Health careSociologyMedicinePsychologyEngineering ethicsPublic relationsLawPolitical science

Abstract

fetched live from OpenAlex

In this paper we argue that nurse leaders need to work actively to create morally supportive environments for nurses in Canada that provide adequate room to exercise conscientious objection. Morally supportive environments engender a safe atmosphere to engage in open dialogue and action regarding conflict of conscience. The CNA's 2008 Code of Ethics for Registered Nurses has recognized the importance of conscientious objection in nursing and has created key guidelines for the registered nurse to follow when a conflict in conscience is being considered or declared. Nurse leaders need to further develop the understanding of conflicts of conscience through education, well-written guidelines for conscientious objection in workplaces and engagement in research to uncover underlying barriers to the enactment of conscientious objections. With advancements in technology, changing healthcare policies and increasing scope of practice, both reflection and dialogue on conscientious objection are critical for the continuing moral development of nurses 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)0.067
metaresearch head score (Gemma)0.109
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.067
Threshold uncertainty score0.355

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0670.109
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0190.123
Scholarly communication0.0200.028
Open science0.0050.022
Research integrity0.0230.050
Insufficient payload (model declined to judge)0.0040.001

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 head0.458
GPT teacher head0.501
Teacher spread0.043 · 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 designTheoretical or conceptual
Domainnot available
GenreCommentary

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

Citations18
Published2010
Admission routes3
Has abstractyes

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