Kidney Dyads: Caregiver Burden and Relationship Strain Among Partners of Dialysis and Transplant Patients
Bibliographic record
Abstract
Background. Caring for dialysis patients is difficult, and this burden often falls on a spouse or cohabiting partner (henceforth referred to as caregiver-partners). At the same time, these caregiver-partners often come forward as potential living kidney donors for their loved ones who are on dialysis (henceforth referred to as patient-partners). Caregiver-partners may experience tangible benefits to their well-being when their patient-partner undergoes transplantation, yet this is seldom formally considered when evaluating caregiver-partners as potential donors. Methods. To quantify these potential benefits, we surveyed caregiver-partners of dialysis patients and kidney transplant (KT) recipients (N = 99) at KT evaluation or post-KT. Using validated tools, we assessed relationship satisfaction and caregiver burden before or after their patient-partner’s dialysis initiation and before or after their patient-partner’s KT. Results. Caregiver-partners reported increases in specific measures of caregiver burden (P = 0.03) and stress (P = 0.01) and decreases in social life (P = 0.02) and sexual relations (P < 0.01) after their patient-partner initiated dialysis. However, after their patient-partner underwent KT, caregiver-partners reported improvements in specific measures of caregiver burden (P = 0.03), personal time (P < 0.01), social life (P = 0.01), stress (P = 0.02), sexual relations (P < 0.01), and overall quality of life (P = 0.03). These improvements were of sufficient impact that caregiver-partners reported similar levels of caregiver burden after their patient-partner’s KT as before their patient-partner initiated dialysis (P = 0.3). Conclusions. These benefits in caregiver burden and relationship quality support special consideration for spouses and partners in risk-assessment of potential kidney donors, particularly those with risk profiles slightly exceeding center thresholds.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".