Uptake and Scalability of a Peritoneal Dialysis Virtual Care Solution: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Early research in the area of virtual care solutions with peritoneal dialysis (PD) patients has focused on evaluating the outcomes and impact of these solutions. There has been less attention focused on understanding the factors influencing the uptake, usability, and scalability of virtual care for chronic kidney disease (CKD) patients receiving PD at home. OBJECTIVE: In this context, a study was undertaken to (1) assess and understand the factors influencing the uptake of a virtual care solution and (2) provide recommendations for the scalability of a virtual care solution aimed at enhancing CKD patients' outcomes and experiences. METHODS: This study used a qualitative design with semistructured interviews and a thematic analysis approach. A total of 25 stakeholders-6 patients and 3 caregivers, 6 health care providers, 2 vendors, and 8 health system decision makers-participated in this study. RESULTS: The following three primary mechanisms emerged to influence the usability of the virtual care solution: (1) receiving hands-on training and ongoing communication from a supportive team, (2) adapting to meet user needs and embedding them into workflow, and (3) being influenced by patient and caregiver characteristics. Further, two overarching recommendations were developed for considerations around scalability: (1) co-design locally, embed into the daily workflow, and deploy over time and (2) share the benefits and build the case. CONCLUSIONS: Study findings can be used by key stakeholders in their future efforts to enhance the implementation, uptake, and scalability of virtual care solutions for CKD and managing PD at home.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".