Video Visits Using the Zoom for Healthcare Platform for People Receiving Maintenance Hemodialysis and Nephrologists: A Feasibility Study in Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: Demand for virtual visits (an online synchronous medical appointment between a health care provider and patient) is increasing due to the COVID-19 pandemic. There may be additional benefits of virtual visits as they appear to be convenient and potentially cost-saving to patients. People receiving maintenance hemodialysis require ongoing care from their nephrologist and may benefit from virtual visits; however, the optimal model for a virtual kidney clinic is unknown. OBJECTIVE: To codesign and assess the feasibility of a virtual (video) kidney clinic model with clinic staff, nephrologists, and patients receiving maintenance hemodialysis, to be used for routine follow-up visits. DESIGN: Mixed-methods study. SETTING: Two main kidney clinics in central Calgary, Alberta. PARTICIPANTS: Adults with kidney failure receiving maintenance hemodialysis, nephrologists, and clinic staff. METHODS: First, we individually interviewed clinic staff and nephrologists to assess the needs of the clinic to deliver virtual visits. Then, we used participant observation with patients and nephrologists to codesign the virtual visit model. Finally, we used structured surveys to evaluate the patients' and nephrologists' experiences when using the virtual model. RESULTS: Eight video visits (8 patients; 6 nephrologists) were scheduled between October 2019 and February 2020 and 7 were successfully completed. Among completed visits, all participants reported high satisfaction with the service, were willing to use it again, and would recommend it to others. Three main themes were identified with respect to factors influencing visit success: IT infrastructure, administration, and process. LIMITATIONS: Patients received training on how to use the videoconference platform by the PhD student, whom also set up the technical components of the visit for the nephrologist. This may have overestimated the feasibility of virtual visits if this level of support is not available in future. Second, interviews were not audio-recorded and thematic analysis relied on field notes. CONCLUSIONS: Video visits for routine follow-up care between people receiving hemodialysis and nephrologists were acceptable to patients and nephrologists. Video visits appear to be feasible if clinics are equipped with appropriate equipment and IT infrastructure, physicians are remunerated appropriately, and patients receive training on how to use software as needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".