Benefits and Challenges When Implementing Incremental Hemodialysis: A Qualitative Study of Patients and Providers
Bibliographic record
Abstract
Background: In 2022, we implemented an incremental hemodialysis (iHD) protocol to initiate twice-weekly treatment for eligible patients. This patient-centered approach aims to ease the transition to dialysis and enhance quality of life. However, limited data exists on how iHD is experienced by patients and health care providers (HCPs). Objective: The aim of the study was to explore the benefits and challenges of iHD from patients' and HCPs' perspectives, and to generate practical considerations for its implementation. Study Design: We conducted an exploratory descriptive qualitative study, guided by an interpretivist-constructivist paradigm, using semi-structured interviews (March-May 2024). Setting: The study was conducted in a tertiary care center. Participants: Participants included patients who were actively or had previously received iHD and HCPs caring for iHD patients. Methods: Interview data was analyzed thematically using inductive thematic analysis. Results: Ten patients and five HCPs were interviewed. Six major themes were identified: (1) better quality of life than conventional hemodialysis, (2) travel and financial benefits, (3) psychosocial and emotional impact similar to conventional hemodialysis, (4) coordination of care and logistics, (5) knowledge and training challenges, and (6) challenges when switching modality. Patients preferred iHD because it afforded them more time for participation in daily life activities. However, the start of a dialysis treatment remained "traumatic" for some patients. While HCPs recognized the greater quality of life for iHD patients, HCPs expressed a need for increased monitoring to ensure adequate care. Patients noticed an inconsistency in care coordination and reduced opportunities to see nephrologists. Some HCPs reported a lack of guidance on iHD. Finally, HCPs observed patients negotiating to stay on iHD even when it became unsafe. Limitations: The small sample size and single-center setting may limit the findings' transferability. Conclusions: IHD was shown to offer quality of life advantages. However, the transition to iHD remained emotionally challenging for patients. Patients often exhibited resistance when moving from twice-weekly to a thrice-weekly schedule. Logistical issues for HCPs and educational barriers must be addressed to optimize delivery of iHD.
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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 | 0.023 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| 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".