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Record W2069616438 · doi:10.7182/pit2012324

Priority in Organ Allocation to Previously Registered Donors: Public Perceptions of the Fairness and Effectiveness of Priority Systems

2012· article· en· W2069616438 on OpenAlexaff
Jennifer A. Chandler, Jacquelyn Burkell, Sam D. Shemie

Bibliographic record

VenueProgress in Transplantation · 2012
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsMcGill UniversityCanadian Blood ServicesMontreal Children's HospitalUniversity of OttawaWestern University
Fundersnot available
KeywordsOrgan donationReciprocity (cultural anthropology)DonationAppealContext (archaeology)ResentmentPerceptionSocial psychologyPreferenceMedicinePublic relationsPsychologyTransplantationPolitical scienceLawEconomicsMicroeconomicsSurgery

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.372

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.302
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations21
Published2012
Admission routes1
Has abstractyes

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