Development of a Survey to Identify Barriers to Living Donation in Kidney Transplant Candidates
Bibliographic record
Abstract
Background Kidney transplantation from living donors, compared with deceased donors, has improved health care outcomes for patients with end-stage renal disease; however, less than 40% of transplants come from living donors. Numerous barriers may impede the identification of, and transplantation from, living donors. Objective To develop and validate a survey to identify barriers that transplant candidates may encounter when seeking a living donor for kidney transplantation. Methods The survey was developed in 3 phases: item identification by using persons with a stake in the process to identify key components; survey refinement, including assessment of content and face validity; and assessment of test-retest reliability by using the kappa coefficient and percent agreement for each of the scaled response items. Results The final survey contained 10 items with a Likert scale response and 5 open-ended questions. Expert nephrologists in the field confirmed face validity and content validity of the survey. The overall kappa coefficient for the scale was 0.76, reflecting excellent agreement, with an overall percent agreement of 88.7%. Conclusion We developed a survey to identify barriers that kidney transplant candidates may experience when seeking a living donor, which demonstrated content and face validity as well as reproducibility. This survey can by used by end-stage renal disease programs to identify barriers in candidates seeking a transplant. The results of the survey can be used to develop interventions to overcome such barriers with an ultimate goal of increasing rates of living kidney donation.
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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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".