Informing transplant candidate and donor education in living kidney donation: mapping educational needs through a rapid review
Bibliographic record
Abstract
OBJECTIVES: Living donor kidney transplantation (LDKT) is a complex medical procedure requiring extensive education for both donors and transplant candidates. With technological advances in healthcare, video educational resources are becoming more widely used. This study aimed to synthesize the existing qualitative evidence on LDKT educational experiences, preferences, and needs from the perspectives of kidney transplant candidates and recipients, donors, and HCPs, to establish the essential LDKT education considerations for candidates and potential donors interested in kidney transplantation. METHODS: A rapid review of qualitative studies on LDKT educational needs was conducted. A literature search was undertaken across MEDLINE, Embase, and CINAHL databases from 2013 to 2023. Cochrane Rapid Reviews Methods Group guidance was utilized. RESULTS: Of 1,802 references, 27 qualitative studies were eligible for inclusion. Qualitative data was analyzed from 803 transplant candidates/recipients, 512 living donors, 104 healthcare providers, and 102 family/friends. Three main themes were identified, including Extensive LDKT Education Throughout Treatment; Shared Learning, Social Support, and Family Dynamics in LDKT; and Diversity and Inclusivity for Minorities. CONCLUSIONS: Improvements and innovations are needed regarding LDKT education for kidney transplant candidates, donors, and support networks.
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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 | 0.018 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".