Professional responsibility, nurses, and conscientious objection: A framework for ethical evaluation
Bibliographic record
Abstract
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.
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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 arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
| opus | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
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 | 0.094 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.008 | 0.111 |
| Scholarly communication | 0.019 | 0.026 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.009 | 0.008 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".