Patient and Clinician Experiences With the Combination of Virtual and In-Person Chronic Kidney Disease Care Since the COVID-19 Pandemic
Bibliographic record
Abstract
Background: Following onset of the COVID-19 pandemic, chronic kidney disease (CKD) clinics in BC shifted from established methods of mostly in-person care delivery to virtual care (VC) and thereafter a hybrid of the two. Objectives: To determine strengths, weaknesses, quality-of-care delivery, and key considerations associated with VC usage to inform optimal way(s) of integrating virtual and traditional methods of care delivery in multidisciplinary kidney clinics. Design: Qualitative evaluation. Setting: British Columbia, Canada. Participants: Patients and health care providers associated with multidisciplinary kidney care clinics. Methods: Development and delivery of semi-structured interviews of patients and health care providers. Results: 11 patients and/or caregivers and 12 health care providers participated in the interviews. Participants reported mixed experiences with VC usage. All participants foresaw a future where both VC and in-person care was offered. A reported benefit of VC was convenience for patients. Challenges identified with VC included difficulty establishing new therapeutic relationships, and variable of abilities of both patients and health care providers to engage and communicate in a virtual format. Participants noted a preference for in-person care for more complex situations. Four themes were identified as considerations when selecting between in-person and VC: person's nonmedical context, support available, clinical parameters and tasks to be completed, and clinic operations. Participants indicated that visit modality selection is an individualized and ongoing process involving the patient and their preferences which may change over time. Health care provider participants noted that new workflow challenges were created when using both VC and in-person care in the same clinic session. Limitations: Limited sample size in the setting of one-on-one interviews and use of convenience sampling which may result in missing perspectives, including those already facing challenges accessing care who could potentially be most disadvantaged by implementation of VC. Conclusions: A list of key considerations, aligned with quality care delivery was identified for health care providers and programs to consider as they continue to utilize VC and refine how best to use different visit modalities in different patient and clinical situations. Further work will be needed to validate these findings and evaluate clinical outcomes with the combination of virtual and traditional modes of care delivery. Trial registration: Not registered.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| 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".