Use of Sharesource in Remote Patient Management in Peritoneal Dialysis: A Canadian Nurse’s Perspective
Bibliographic record
Abstract
Remote patient management (RPM) via 2-way connectivity addresses many challenges that the peritoneal dialysis (PD) renal care team faces when treating home dialysis patients. It addresses psychological barriers and social determinants of health by permitting self-care, increased independence, and enabling patients to remain in their community. RPM lends opportunities for patient's empowerment in self-care and treatment decisions. AMIA cycler with remote monitoring, step-by-step voice guidance, and graphic interface all contribute to patient engagement and empowerment in the Canadian home dialysis setting. OBJECTIVES: To describe a series of unique patient cases in the realm of PD and how adopting new technology has enabled patients and clinicians to rise above challenging circumstances to optimize home dialysis, especially in patients living in remote communities. METHODS: With the introduction of RPM at Seven Oaks Hospital in Winnipeg, MB, nurses have tracked and documented examples of success on home dialysis. Despite obstacles, patients embraced self-care in the home setting with increased confidence. RESULTS: Included are patients who were provided support to perform reliable home dialysis with AMIA cycler with Sharesource that offers voice guidance, graphic interface, and 2-way connectivity. Patients overcame the challenges of self-care in a remote setting with physical impairments, as well as enhanced acceptance of home dialysis. The utilization of RPM by the care team promoted patient independence and confidence in performing therapy at home. CONCLUSIONS: Our experience with this technology demonstrates an increase in patient confidence in training and RPM of home dialysis. We have provided specific case examples of patient engagement and empowerment leading to improved self-care. New technology can address psychological barriers and social determinants of health in home dialysis patients.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".