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Record W2324720945 · doi:10.1215/03616878-2334674

Attitudes toward Reciprocity Systems for Organ Donation and Allocation for Transplantation: Table 1

2013· article· en· W2324720945 on OpenAlexaffabout
Jacquelyn Burkell, Jennifer A. Chandler, Sam D. Shemie

Bibliographic record

VenueJournal of Health Politics Policy and Law · 2013
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsWestern University
Fundersnot available
KeywordsReciprocity (cultural anthropology)DonationOrgan donationContext (archaeology)Social psychologyFocus groupNorm of reciprocityMedicineTransplantationPsychologyPolitical scienceSurgeryLawSociology

Abstract

fetched live from OpenAlex

Many of those who support organ donation do not register to become organ donors. The use of reciprocity systems, under which some degree of priority is offered to registered donors who require an organ transplant, is one suggestion for increasing registration rates. This article uses a combination of survey and focus group methodologies to explore the reaction of Canadians to a reciprocity proposal. Our results suggest that the response is mixed. Participants are more convinced of the efficacy than they are of the fairness of a reciprocity system. Those more positive about donation (decided donors and those leaning toward donation) rate the system more positively. Although there is general endorsement of the notion that those who wish to receive should be prepared to give (the Golden Rule), this does not translate into universal support for a reciprocity system. In discussions of efficacy, decided donors focus on the positive impact of reciprocity, whereas undecided donors also reflect on the limits of reciprocity for promoting registration. The results demonstrate divided support for reciprocity systems in the Canadian context, with perceptions of efficacy at the cost of fairness. Further studies are warranted prior to considering a reciprocity system in Canada.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.874
Threshold uncertainty score0.270

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.051
GPT teacher head0.366
Teacher spread0.315 · 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 designTheoretical or conceptual
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

Citations22
Published2013
Admission routes2
Has abstractyes

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