Reimbursing Live Organ Donors for Incurred Non-Medical Expenses: A Global Perspective on Policies and Programs
Bibliographic record
Abstract
Methods to reimburse living organ donors for the non-medical expenses they incur have been implemented in some jurisdictions and are being considered in others. A global understanding of existing legislation and programs would help decision makers implement and optimize policies and programs. We searched for and collected data from countries that practice living organ donation. We examined legislation and programs that facilitate reimbursement, focusing on policy mechanisms, eligibility criteria, program duration and types of expenses reimbursed. Of 40 countries, reimbursement is expressly legal in 16, unclear in 18, unspecified in 6 and expressly prohibited in 1. Donor reimbursement programs exist in 21 countries; 6 have been enacted in the last 5 years. Lost income is reimbursed in 17 countries, while travel, accommodation, meal and childcare costs are reimbursed in 12 to 19 countries. Ten countries have comprehensive programs, where all major cost categories are reimbursed to some extent. Out-of-country donors are reimbursed in 10 jurisdictions. Reimbursement is conditional on donor income in 7 countries, and recipient income in 2 countries. Many nations have programs that help living donors with their financial costs. These programs differ in operation and scope. Donors in other regions of the world are without support.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".