Geriatric Specialists’ Perspectives on Telemedicine during the COVID-19 Pandemic: a Concurrent Triangulation Mixed-Methods Study*
Bibliographic record
Abstract
During the COVID-19 pandemic, physicians provided virtual care to minimize viral transmission. This concurrent triangulation mixed-methods study assesses the use of synchronous telephone and video visits with patients and asynchronous eConsults by geriatric providers, and explores their perspectives on telemedicine use during the pandemic. Participants included physicians practicing in Ontario, Canada who were certified in Geriatric Medicine, or Care of the Elderly, or who were the most responsible physician in a long-term care for at least 10 patients. Participants' perspectives were solicited using an online survey and themes were generated through a reflexive thematic analysis of survey responses. We assessed the current use of each telemedicine tool and compared the proportion of participants using telemedicine before the pandemic with self-predicted use after the pandemic. We received 29 surveys from eligible respondents (87.9% completion rate), with 75.9% being geriatricians. The telephone was most used (96.6%), followed by video (86.2%) and eConsults (64%). Most participants using telephone and video visits had newly implemented them during the pandemic and intend to continue using these tools post-pandemic. Our thematic analysis revealed that telemedicine plays an important role in the continuity of care during the pandemic, with increased self-reported positive perspectives and openness towards use of virtual care tools, although limited by inadequate physical exams or cognitive testing. Its ongoing use depends on the availability of continued remuneration.
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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 | 0.049 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".