The Use of Telemedicine in Older-Adults During the COVID-19 Pandemic: a Weekly Cross-Sectional Analysis in Ontario, Canada
Bibliographic record
Abstract
The COVID-19 pandemic led to rapid adoption of telemedicine for health-care service delivery. There are concerns that older adults, the highest users of the health-care system, would be left behind because of this shift. It remains unclear how the pandemic impacted telemedicine and other health-care service use in this group. We conducted a population-based, weekly cross-sectional study using administrative data from Ontario, Canada. Telemedicine use was measured for the overall older-adult population aged 65+ and across sociodemographic groups from January 2018 to March 2021. We also assessed the use of key health-care services between high and low patient users of telemedicine who were diagnosed with dementia. We found that telemedicine visits outnumbered in-person visits in older adults during the pandemic (average of 74 vs. 62 visits per 1000 per week). Of all specialties, psychiatrists delivered the most telemedicine visits, reaching 90% of visits in a week. Higher rates of telemedicine use during COVID-19 were found for patients who resided in urban regions (84 visits per 1000 per week), but no differences were found across income quintiles. Among dementia patients, high telemedicine users had higher health-care utilization than low telemedicine users (i.e., 21,108 vs. 3,276 outpatient visits per week) during the pandemic. Findings suggest that telemedicine was crucial in helping older adults, a group most vulnerable to COVID-19, maintain access to care during the pandemic. Telemedicine presents an important opportunity for older adults; however, future research should focus on barriers to equitable access and quality of care provided through telemedicine.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".