Virtual care use among older immigrant adults in Ontario, Canada during the COVID-19 pandemic: A repeated cross-sectional analysis
Why this work is in the frame
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Bibliographic record
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 was low (weekly average <2 visits per 1000) prior to the pandemic, but 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 per 1000). As the pandemic progressed, the rates between these groups became similar (80 vs 79 visits per 1000). Virtual care use was consistently lower among immigrants in the family class (75 visits per 1000) compared to the economic (82 visits per 1000) or refugee (89 visits per 1000) classes, and was lower among those who only spoke French (69 visits per 1000) or neither French nor English (73 visits per 1000) compared to those who were fluent in English (81 visits per 1000). 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.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 it