Virtual care use among older immigrant adults in Ontario, Canada during the COVID-19 pandemic: a repeated cross-sectional analysis
Bibliographic record
Abstract
Abstract The critical role of virtual care during the COVID-19 pandemic has raised concerns about the widening disparities to access by vulnerable populations including older immigrants. This paper aims to describe virtual care use in older immigrant populations residing in Ontario, Canada. In this population-based, repeated cross-sectional study, we used linked administrative data to describe virtual care and healthcare utilization among immigrants aged 65 years and older before and during the COVID-19 pandemic. Visits were identified weekly from January 2018 to March 2021 among various older adult immigrant populations. Among older immigrants, over 75% were high users of virtual care (had two or more virtual visits) during the pandemic. Rates of virtual care use increased for both older adult immigrant and non-immigrant populations. At the start of the pandemic, virtual care use was lower among immigrants compared to non-immigrants (weekly average of 77 vs 86 visits). As the pandemic progressed, the rates between these groups became similar (80 vs 79 visits). Virtual care use was consistently lower among immigrants in the family class (75 visits) compared to the economic (82 visits) or refugee (89 visits) classes, and was lower among those who only spoke French (69 visits) or neither French nor English (73 visits) compared to those who were fluent in English (81 visits). This study found that use of virtual care was comparable between older immigrants and non-immigrants overall, though there may have been barriers to access for older immigrants early on in the pandemic. However, within older immigrant populations, immigration category and language ability were consistent differentiators in the rates of virtual care use throughout the pandemic. Author Summary When the COVID-19 pandemic began, healthcare systems pivoted from in-person to virtual care to maintain physical distancing. Studies have shown that virtual care use became much more frequent during the pandemic as a result. What we do not know is whether virtual care is being used equitably, that is, whether everybody has fair access to the resource. This can be a big issue particularly amongst older adults, who are often battling several diseases and use healthcare frequently. Many older adults are immigrants who may face challenges in accessing healthcare due to reasons such as limited language fluency and resource support. Our study found that older adult immigrants aged 65 and above living in Ontario, Canada had lower use of virtual care initially, but their use eventually caught up with non-immigrants as the pandemic progressed. We also found that older adult immigrants from the family class had lower virtual care use compared to those from the economic, refugee, or other immigration classes. Additionally, immigrants who were not fluent in English had lower use compared to those who were fluent. These results show that virtual care access remains an issue for vulnerable minorities and steps should be taken to ensure these groups are receiving adequate care.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".