Allocation of transplantable organs: Do people want to punish patients for causing their illness?
Bibliographic record
Abstract
Some people believe patients with alcoholic cirrhosis should not receive equal priority for scarce transplantable organs. This may reflect a belief that these patients (1) are personally responsible for causing their own illnesses, (2) have poor transplant prognoses, or (3) are unworthy because they have engaged in socially undesirable behavior. We explore the roles that social desirability and personal responsibility have in people's judgments about transplant allocation. We presented prospective jurors with 4 scenarios, asking them to distribute 100 transplantable organs among 2 groups of 100 patients each. In each scenario, 1 group of patients, but not the other, was described as having a history of unhealthy behavior (alcohol or cigarette use) associated with a poorer prognosis. In some scenarios, alcohol or cigarette use was said to cause the organ failure. In others, it only contributed to the patients' transplant prognosis. We also obtained self-reports of subjects' own smoking status. Subjects allocated significantly fewer than half the organs to those with unhealthy behaviors and worse prognoses (33%; P <.001), but the specific behavior (alcohol versus cigarette use) was not significantly associated with subjects' allocation choices. Significantly fewer organs were allocated to patients with behavior responsible for causing their diseases than to other patients (P <.0001). Subjects who never smoked discriminated the most and current smokers discriminated the least against patients with a history of unhealthy behavior (P <.0001). The public's transplantation allocation preferences are influenced by whether patients' behaviors are said to have caused their organ failure.
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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 | 0.015 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".