Deceased‐directed donation: Considering the ethical permissibility in a multicultural setting
Bibliographic record
Abstract
This paper explores the ethics of deceased-directed donation (DDD) and brings a unique perspective to this issue-the relevance of providing family-centered care and culturally sensitive care to deceased donors, potential recipients, and their families. The significance of providing family-centered care is becoming increasingly prevalent, specifically in pediatric healthcare settings. Therefore, this topic is especially relevant to those working with and interested in pediatrics. As the world is becoming more diverse with globalization, assessing the cultural aspect of the ethics of DDD is increasingly salient. We provide a brief overview of DDD across the globe, review prominent arguments both for and against DDD, consider family-centered and culturally specific considerations, and offer considerations for the development of a policy or guideline. We determine that the practice of DDD is ethically defensible in certain circumstances and congruent with providing both family-centered and culturally sensitive care. Our analysis is relevant to any country with a diverse population and any healthcare provider or institution that operates under a framework of family-centered care, such as those in pediatric hospitals.
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 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 | 0.091 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.038 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.006 | 0.013 |
| 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".