Supporting ethical ICU nursing practice in organ donation: An analysis of personhood
Bibliographic record
Abstract
Organ donation is critical to the survival of thousands of people waiting for an organ transplant. While nurses take responsibility for patient and family comfort and quality of dying whenever patients die in the intensive care unit (ICU), organ donation presents unique challenges to these ideals. Critical care settings are essential for organ donation, where candidates for donation are identified, referred, stabilized, and maintained until organs can be retrieved. Nurses may feel challenged in completing organ donation-related tasks without forsaking values and practices that are typically associated with a palliative nursing approach and the achievement of a ‘good death’ in the ICU. Further, the moral uncertainty in this context could be increasing, given the advent of a new pathway for organ donation in ICU: medical assistance in dying (MAiD). In this paper, we reflect on the ethical meanings, challenges and possibilities of a good death in the context of organ donation in ICU nursing. We argue that personhood—as a conceptual frame to guide nursing practice—offers one way of reconciling seemingly disparate values and practices. To illustrate the importance of integrating this concept, we draw on real-life case studies from clinical ICU nursing. These examples showcase the emotional engagement and moral tension that nurses could experience when caring for people who occupy a liminal space between life and death. Ultimately, we hope this paper will inspire and support ethical reflection and action among ICU nurses caring for organ donors.
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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 | 0.024 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.033 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| 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".