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Record W4383615433 · doi:10.1177/09697330231180749

Professional responsibility, nurses, and conscientious objection: A framework for ethical evaluation

2023· article· en· W4383615433 on OpenAlexaff
Pamela J. Grace, Elizabeth Peter, Vicki D. Lachman, Norah L. Johnson, Deborah J. Kenny, Lucia D. Wocial

Bibliographic record

VenueNursing Ethics · 2023
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCLARITYEngineering ethicsNursing ethicsConscienceHarmNursingVariety (cybernetics)Professional responsibilityAction (physics)PsychologySociologyMedicineLawPolitical scienceSocial psychologyComputer science

Abstract

fetched live from OpenAlex

Conscientious objections (CO) can be disruptive in a variety of ways and may disadvantage patients and colleagues who must step-in to assume care. Nevertheless, nurses have a right and responsibility to object to participation in interventions that would seriously harm their sense of integrity. This is an ethical problem of balancing risks and responsibilities related to patient care. Here we explore the problem and propose a nonlinear framework for exploring the authenticity of a claim of CO from the perspective of the nurse and of those who must evaluate such claims. We synthesized the framework using Rest's Four Component Model of moral reasoning along with tenets of the International Council of Nursing's (ICN) Code of Ethics for Nurses and insights from relevant ethics and nursing ethics literature. The resulting framework facilitates evaluating potential consequences of a given CO for all involved. We propose that the framework can also serve as an aid for nurse educators as they prepare students for practice. Gaining clarity about the sense in which the concept of conscience provides a defensible foundation for objecting to legally, or otherwise ethically, permissible actions, in any given case is critical to arriving at an ethical and reasonable plan of action.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gptno category
Domain: not available · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptualhigh
opusno category
Domain: not available · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptualhigh
models agreeAgreement compares identical category sets and study designs across arms.

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)0.074
metaresearch head score (Gemma)0.321
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesMetaresearch, Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.786
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0740.321
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0040.024
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 head0.376
GPT teacher head0.653
Teacher spread0.277 · 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

Labeled directly by 2 models reading the full record.

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

Citations26
Published2023
Admission routes1
Has abstractyes

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