Conflicts of conscience in the neonatal intensive care unit: Perspectives of Alberta
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.029 | 0.017 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".