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Record W2605872354 · doi:10.1177/0969733016684547

Conflicts of conscience in the neonatal intensive care unit: Perspectives of Alberta

2017· article· en· W2605872354 on OpenAlexafffundabout
Natalie J Ford, Wendy Austin

Bibliographic record

VenueNursing Ethics · 2017
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsUniversity of Alberta
FundersUniversity of Alberta
KeywordsConscienceNeonatal intensive care unitIntensive care unitNursingPsychologyMedicineEnvironmental ethicsPolitical scienceLawPsychiatryPhilosophy

Abstract

fetched live from OpenAlex

BACKGROUND:: Limited knowledge of the experiences of conflicts of conscience found in nursing literature. OBJECTIVES:: To explore the individual experiences of a conflict of conscience for neonatal nurses in Alberta. RESEARCH DESIGN:: Interpretive description was selected to help situate the findings in a meaningful clinical context. PARTICIPANTS AND RESEARCH CONTEXT:: Five interviews with neonatal nurses working in Neonatal Intensive Care Units throughout Alberta. ETHICAL CONSIDERATION:: Ethics approval from the Health Research Ethics Board at the University of Alberta. FINDINGS:: Three common themes emerged from the interviews: the unforgettable conflict with pain and suffering, finding the nurse's voice, and the unique proximity of nurses. DISCUSSION AND CONCLUSION:: The nurses described a conflict of conscience when the neonate in their care experienced undermanaged pain and unnecessary suffering. During these experiences, they felt guilty, sad, hopeless, and powerless when they were unable to follow their conscience. Informal ways to follow their conscience were employed before declaration of conscientious objection was considered. This study highlights the vital importance of respecting a conflict of conscience to maintain the moral integrity of neonatal nurses and exposes the complexities of conscientious objection.

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 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)0.007
metaresearch head score (Gemma)0.208
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.403
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.208
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.005
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.009
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 head0.344
GPT teacher head0.590
Teacher spread0.246 · 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; both teacher heads agree on what is shown here.

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

Citations28
Published2017
Admission routes3
Has abstractyes

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