A Qualitative Exploration of Patient-Provider Communication Challenges After a Kidney Transplant
Bibliographic record
Abstract
Background: Kidney transplantation is a life-altering treatment, but symptoms and drug side effects persist for many patients post-transplant. Effective communication with the healthcare team (HCT) is key to address these, yet research shows patients find that challenging. Ineffective communication may lead to inadequate assessment and management of symptoms. Although communication is among patients' top research priorities, less than 5% of articles in the two leading transplant journals address it. To fill this gap, we conducted a qualitative exploration of communication challenges from recipients' perspective. Methods: Within a larger study, we used Qualitative Description methodology to understand the quality of communication between patients and HCT post-transplant. Purposive recruitment was done via flyers (Jun-Dec2020). Patients with significant cognitive impairment or insufficient English were excluded. In-depth, semi-structured, individual interviews were recorded and transcribed verbatim. Directed content analysis framed the iterative development of codes. Results: 7 recipients (4 males, ages 51-75, 4-15.5 years post-transplant) and 1 caregiver participated. Findings indicate a range of experiences, from regular contact with HCT to little perceived opportunity to communicate in-between clinic visits. Compared to pre-transplant care, communication was less frequent and many patients felt isolated, making it difficult for them to know where and how to seek information and support. Instead, patients relied on searching for information online, visiting family doctors or the emergency room. Some used phone/voicemail to reach HCT, but these were not always timely or efficient. Patients also raised the need for HCT to consider the uniqueness of each patient and their broader context, in addition to quantitative measures, in assessing their health. The diverse communication experiences also relate to patients' comfort with self-advocacy. While some proactively initiate conversation, others are more reserved. Conclusions: Communication challenges between kidney transplant recipients and HCT contribute to feelings of isolation and difficulties navigating post-transplant life. Tailoring communication to individual preferences may improve patient-centered 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 | 0.022 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".