Exploring the utility and scalability of a telehomecare intervention for patients with chronic kidney disease undergoing peritoneal dialysis—a study protocol
Bibliographic record
Abstract
BACKGROUND: Chronic Kidney Disease (CKD) is a pressing global health concern that is placing increased strain on health care resources. CKD patients regularly receive peritoneal dialysis as a common CKD treatment. An emerging technological solution is telehomecare as way to support patients receiving PD in their homes. This study protocol outlines a mixed methods evaluation exploring a telehomecare developed to enhance CKD patients' outcomes and experiences. The study aims to assess the usability, acceptability and scalability of this virtual care application. METHODS: A realist evaluation using an embedded case study design will be used to understand the usability, acceptability and scalability of a telehomecare application for patients with CKD undergoing PD. The realist evaluation that is further described in this paper is part of a larger evaluation of the eQ Connect™ intervention that includes a randomized, parallel-arm control trial aimed at determining if utilizing eQ Connect improves selected clinical outcomes for PD patients (CONNECT Trial). DISCUSSION: Potential implications of this study include elucidating which components of the intervention are most effective and under what conditions with a focus on the contextual influences. Collectively, our multi-method design will yield knowledge around how best to implement, sustain and spread the telehomecare application that will be useful to guide the development, implementation and evaluation of future virtual care applications aimed at improving the quality of care outcomes and experiences of patients. TRIAL REGISTRATION: NCT02670512 . Registered: January 18, 2016.
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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 | 0.031 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.030 | 0.005 |
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".