Nurses’ use of conscientious objection and the implications for conscience
Bibliographic record
Abstract
AIMS: To explore the meaning of conscience for nurses in the context of conscientious objection (CO) in clinical practice. DESIGN: Interpretive phenomenology was used to guide this study. DATA SOURCES: Data were collected from 2016 - 2017 through one-on-one interviews from eight nurses in Ontario. Iterative analysis was conducted consistent with interpretive phenomenology and resulted in thematic findings. REVIEW METHODS: Iterative, phased analysis using line-by-line and sentence highlighting identified key words and phrases. Cumulative summaries of narratives thematic analysis revealed how nurses made meaning of conscience in the context of making a CO. RESULTS: Conscience issues and CO are current, critical issues for nurses. For Canadian nurses this need has been recently heightened by the national legalization of euthanasia, known as Medical Assistance in Dying in Canada. Ethics education, awareness, and respect for nurses' conscience are needed in Canada and across the profession to support nurses to address their issues of conscience in professional practice. CONCLUSION: Ethical meaning emerges for nurses in their lived experiences of encountering serious ethical issues that they need to professionally address, by way of conscience-based COs. IMPACT: This is the first study to explore what conscience means to nurses, as shared by nurses themselves and in the context of CO. Nurse participants expressed that support from leadership, regulatory bodies, and policy for nurses' conscience rights are indicated to address nurses' conscience issues in practice settings.
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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 | 0.020 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.049 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".