Priority in Organ Allocation to Previously Registered Donors: Public Perceptions of the Fairness and Effectiveness of Priority Systems
Bibliographic record
Abstract
A priority system is one in which previously registered donors receive a preference in the allocation of organs for transplant ahead of those who have not registered. Supporters justify these systems on the basis that they are fair and will encourage donor registration. This article reviews existing studies of public reactions to priority systems, as well as studies of the extent to which the moral principle of reciprocity affects decision making in organ donation. The role of reciprocity in the public discourse surrounding the enactment of priority systems in Singapore and Israel is described. One factor that seems to have been relevant in these countries is the existence of a religious minority that is perceived as willing to take an organ but not to donate one. Although this perception may have fueled a resentment of perceived "free-riders," concerns were raised about the social divisiveness of priority systems. In sum, people appear to be sensitive to the principle of reciprocity in the context of organ donation, but this sensitivity does not always translate into support for priority systems. Further research into whether public messaging about organ donation could be modified to encourage registration by appeal to the golden rule would be worthwhile.
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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 | 0.030 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 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".