Understanding the Healthcare Needs of Living Kidney Donors Using the Picker Principles of Patient-centered Care: A Scoping Review
Bibliographic record
Abstract
Living kidney donors (LKDs) undertake a complex and multifaceted journey when pursuing donation and have several unmet healthcare needs. A comprehensive understanding of these needs across their entire donation trajectory can help develop a patient-centered care model. We conducted a scoping review to synthesize empirical evidence, published since 2000, on LKDs' experiences with healthcare from when they decided to pursue donation to postdonation care, and what they reported as their care needs. We categorized them according to the 8 Picker principles of patient-centered care. Of the 4514 articles screened, 47 were included. Ample literature highlighted the need for (1) holistic, adaptable, and linguistically appropriate approaches to education and information; (2) systematic, consistent, and proactive coordination and integration of care; and (3) self-management and preparation to optimize perioperative physical comfort. Some literature highlighted the need for (4) better continuity and transition of care postdonation. Two key unmet needs were the lack of (5) a holistic psychosocial evaluation predonation and predischarge to provide emotional support and alleviation of fear and anxiety; and (6) access to specialty and psychosocial services postdonation especially when adverse events occurred. Limited literature explored the principles of (7) respect for patients' values, preferences, and expressed needs; and (8) involvement of family and friends as caregivers. We summarize several unmet healthcare needs of LKDs throughout their donation journey and highlight knowledge gaps. Addressing them can improve their well-being and experiences, and potentially address inequities in living kidney donation and increase living donor kidney transplantation.
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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 | 0.012 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| 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".