Identifying the Unmet Healthcare Needs of Kidney Transplant Recipients Who Experience Graft Loss: Learning From Patients’ Experience
Bibliographic record
Abstract
BACKGROUND: Kidney transplant recipients with graft loss (KTR-GL) are an increasing group of patients whose care needs are largely unmet. The lack of patient perspectives is a key research gap. We conducted an in-depth exploration of the experiences of KTR-GL to identify their healthcare needs. METHODS: This qualitative study adopted an interpretive descriptive methodology. Data collection entailed semistructured narrative interviews conducted until data saturation was achieved and was analyzed using inductive thematic analysis. RESULTS: Our sample included 23 KTR-GL (women: 34.8%; mean age, 54.3 y). Six themes were identified that represent areas in which participants' needs may be inadequately acknowledged and/or met: (1) setting expectations (longevity of the graft, transplant is not a cure, risk of graft failure, anticipating transplant loss, and balancing hope and realism), (2) communicating with care team (support and empathy and clarifying the cause of graft failure), (3) support for transition to dialysis (shaped by prior experience, preparedness for the initiation of dialysis, lack of options, and dialysis requires adjustment), (4) navigating the path to retransplantation (understanding patient preferences, clarity and transparency, addressing ineligibility, preemptive transplant, and living donation), (5) psychosocial resources (access to psychological services, specific and adequate psychological support, reliable social worker, and peer support), and (6) lessons learned (building mutual trust, self-advocacy, defining a successful transplant, and gaining resilience). CONCLUSIONS: In this in-depth exploration of the experiences of KTR-GL, we have identified several unmet healthcare needs that have practice and policy implications. Incorporating a patient-centered approach is needed to improve the healthcare experiences and, potentially, the outcomes of KTR-GL.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".