Geriatric care physicians’ perspectives on providing virtual care: a reflexive thematic synthesis of their online survey responses from Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: During the COVID-19 pandemic, telemedicine was widely implemented to minimise viral spread. However, its use in the older adult patient population was not well understood. OBJECTIVE: To understand the perspectives of geriatric care providers on using telemedicine with older adults through telephone, videoconferencing and eConsults. DESIGN: Qualitative online survey study. SETTING AND PARTICIPANTS: We recruited geriatric care physicians, defined as those certified in Geriatric Medicine, Care of the Elderly (family physicians with enhanced skills training) or who were the most responsible physician in a long-term care home, in Ontario, Canada between 22 December 2020 and 30 April 2021. METHODS: We collected participants' perspectives on using telemedicine with older adults in their practice using an online survey. Two researchers jointly analysed free-text responses using the 6-phase reflexive thematic analysis. RESULTS: We recruited 29 participants. Participants identified difficulty using technology, patient sensory impairment, lack of hospital support and pre-existing high patient volumes as barriers against using telemedicine, whereas the presence of a caregiver and administrative support were facilitators. Perceived benefits of telemedicine included improved time efficiency, reduced travel, and provision of visual information through videoconferencing. Ultimately, participants felt telemedicine served various purposes in geriatric care, including improving accessibility of care, providing follow-up and obtaining collateral history. Main limitations are the absence of, or incomplete physical exams and cognitive testing. CONCLUSIONS: Geriatric care physicians identify a role for virtual care in their practice but acknowledge its limitations. Further work is required to ensure equitable access to virtual care for older adults.
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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 | 0.018 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".