The Tangible Benefits of Living Donation: Results of a Qualitative Study of Living Kidney Donors
Bibliographic record
Abstract
The framework currently used for living kidney donor selection is based on estimation of acceptable donor risk, under the premise that benefits are only experienced by the recipient. However, some interdependent donors might experience tangible benefits from donation that cannot be considered in the current framework (ie, benefits experienced directly by the donor that improve their daily life, well-being, or livelihood). METHODS: We conducted semistructured interviews with 56 living kidney donors regarding benefits experienced from donation. Using a qualitative descriptive and constant comparative approach, themes were derived inductively from interview transcripts by 2 independent coders; differences in coding were reconciled by consensus. RESULTS: Of 56 participants, 30 were in interdependent relationships with their recipients (shared household and/or significant caregiving responsibilities). Tangible benefits identified by participants fell into 3 major categories: health and wellness benefits, time and financial benefits, and interpersonal benefits. Participants described motivations to donate a kidney based on a more nuanced understanding of the benefits of donation than accounted for by the current "acceptable risk" paradigm. DISCUSSION: Tangible benefits for interdependent donors may shift the "acceptable risk" paradigm (where no benefit is assumed) of kidney donor evaluation to a risk/benefit paradigm more consistent with other surgical decision-making.
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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 | 0.026 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".