Virtual Care Perceptions and Experiences of Older Adults During COVID-19 in Canada: A Systematic Review
Bibliographic record
Abstract
Background/Objectives: Older adults (65+) are the fastest growing age group in Canada, comprising 18.8% of the country’s population. During the COVID-19 pandemic, use of virtual care, including telehealth and tele-medicine, increased dramatically among older adults in Canada who often face higher health risks, mobility limitations, and many barriers to accessing healthcare. Despite the rapid expansion in virtual care, no systematic review has focused specifically on virtual care among older adults in Canada. This review aims to explore the factors influencing virtual care adoption and the experiences of older Canadians during the pandemic through a systematic review. Methods: Conducted in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, the review involved a comprehensive search of PubMed, Scopus, ESCBOHost, and Web of Science on 2 May 2025, yielding 281 unique citations. After screening and applying eligibility criteria, 15 studies employing quantitative, qualitative, or mixed-methods designs, with sample sizes ranging from 15 to 2,282,798, were included and appraised using the Mixed Methods Appraisal Tool (MMAT). Results: The review identified three domains of factors and the ways in which each factor shapes older adults’ virtual care experiences: (1) personal factors influencing virtual care use and demand (e.g., age, education, language, income, immigration status, community sizes), (2) resource factors impacting virtual care adoption (e.g., technology access, support), and (3) varying virtual care experiences among older adults (e.g., in assessment and communication efficacy, privacy, care quality, convenience, safety, and costs). Conclusions: This review highlights the complexities of virtual care engagement among older adults and underscores the need for inclusive, tailored strategies to improve the accessibility and effectiveness of virtual care delivery in both pandemic and post-pandemic contexts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".