Virtual consultation in kidney care: a mixed-methods study on a model for safe and effective integration into routine clinical care
Bibliographic record
Abstract
RATIONALE AND OBJECTIVE: Globally, the COVID-19 pandemic necessitated a rapid introduction of virtual care delivery via telephone or videoconference. The rapid advancements in e-health technology facilitated options for virtual care, including asynchronous data transfer in virtual clinic models and patient-facing smartphone applications for communications and self-care. However, the clinical benefits of virtual consultation have not been consistently demonstrated in all facets of kidney care, and the adoption of this innovation alters workflows and health professionals' perceptions of care delivery. This study evaluated the integration of virtual outpatient consultation safely and effectively into the kidney care programme in Alberta. STUDY DESIGN: We leveraged a mixed-methods approach to collate data about clinicians' experiences and opinions, forming the basis for the qualitative part of the study. DATA EXTRACTION: Data were collected through surveys, interviews and focus groups of nephrologists and home dialysis nurses. ANALYTICAL APPROACH: Focus group/interview transcripts for nephrologists and nurses were used to generate initial codebooks, which were iteratively refined throughout the analysis. Codes were categorised and analysed thematically, and data collected from nephrologists and nurses were analysed separately. RESULTS: The findings demonstrated that clinicians support the use of routine virtual care. Clinicians' opinions on implementation requirements emphasised logistics for routine virtual care integration, quality of care delivered, impacts on the therapeutic relationship and regulatory policy clarification. LIMITATION: The generalisability of the findings is limited in scope, as the study was conducted in a single nephrology programme in Canada, and may not apply to other provinces or settings. CONCLUSIONS: These findings inform recommendations for safe and effective virtual care delivery and can be leveraged to inform virtual care designs in kidney care programmes. Further study is required to clarify the impacts of virtual care on specific population demographics based on geography (rural vs urban) and age (elderly population) in the post-COVID-19 era, and determine how to effectively integrate patient perspectives into this model of care.
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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 | 0.052 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".