Patient Perspectives of Telemedicine in Outpatient Nephrology Clinics During COVID-19: A Qualitative Study
Bibliographic record
Abstract
Background: The COVID-19 pandemic notably disrupted care for patients with chronic kidney disease (CKD) care, necessitating a rapid shift to telemedicine. Despite the growing use of telemedicine, the impact of this transition on patients' experiences, particularly in Canada and considering sociocultural factors, remains underexplored. This study aims to investigate patients with CKD perspectives on telemedicine versus in-person care and to offer recommendations for enhancing telemedicine services. Objective: The objective was to understand patients with CKD views on telemedicine clinics during the pandemic compared to traditional in-person clinics. Design: This was a qualitative descriptive study employing semi-structured interviews. Setting: This study was conducted in general nephrology and multidisciplinary kidney care clinics in London, Canada. Population: The study population was English-speaking patients with CKD with at least one in-person nephrology visit before March 15, 2020, and one telemedicine appointment after March 30, 2020. Methods: Interviews were conducted using a structured guide, with transcripts analyzed line-by-line by 3 independent reviewers through directed content analysis. Themes were identified and agreed upon through group consensus. Results: Interviews with 12 participants revealed 5 key themes: (1) convenience; (2) building connection and trust; (3) necessity of in-person care; (4) role of family or caregivers; and (5) preferences for clinic types. Most participants (11/12) valued the convenience of telemedicine, noting similar levels of care compared to in-person visits. However, they found it easier to establish personal connections in face-to-face appointments. Most (8/12) preferred in-person visits if their condition worsened. Overall, a combination of in-person and telemedicine was favored, with a preference for video over telephone. Limitations: The study's focus on one academic nephrology center in Ontario and predominantly white participants limits broader applicability. Additionally, recall bias may affect the findings due to the interview-based design. Conclusions: Telemedicine will remain integral to CKD care, with a hybrid model combining in-person and telemedicine preferred. Integrating patient feedback into future telemedicine practices is essential to enhance flexibility, access, and patient satisfaction.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".