Symptom Management Preferences of Kidney Transplant Recipients and Caregivers
Bibliographic record
Abstract
Background: Kidney transplant (KT) recipients frequently experience physical, emotional, and social challenges. These are often undermanaged and can lead to impaired quality of life. Better understanding of the perspectives of KT recipients and their caregivers about their symptom experiences and management needs will improve post-transplant care for KT recipients. Methods: As part of a larger study aimed at developing a patient-centered electronic assessment toolkit, adult (≥18 years) KT recipients and caregivers of KT recipients were recruited for this study via flyers. Patients not fluent in English or cognitively impaired were excluded. Qualitative description was used to explore and understand participants' post-transplant experiences and preferences. A semi-structured interview guide with open-ended questions was used to facilitate in-depth, individual interviews. Interviews were recorded and transcribed verbatim. Transcripts were analyzed via content analysis using deductive and inductive coding strategies. Codes and categories were developed and refined by the research team. Results: Seven KT recipients and one caregiver (age: 52-76 years, 8-20 years post-transplant, 5/8 male) participated. Participants identified significant challenges in physical (e.g. fatigue, sleep disturbances, weight or mobility issues); emotional (e.g. depression, anxiety); and social (e.g. financial challenges, self-care, social roles) domains. Participants considered fatigue as the most troublesome symptom. Furthermore, patients described the clustering of their post-transplant symptoms across domains. For example, fatigue overlapped with depression and the inability to perform self-care activities and maintain relationships. Participants also expressed that their post-transplant care centered on physical symptoms with little exploration and support of psychological and social issues. Finally, participants emphasized that a care plan integrating all aspects of health is needed to adequately support their needs. Conclusions: This analysis identified a range of patient-valued physical, emotional, and social concerns, with fatigue being the most troublesome symptom. These findings will inform the development of future interventions to improve patient-centered post-transplant care.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".