A retrospective evaluation of access equity in virtual care during the COVID-19 pandemic: A 2-year review and comparison of visits in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Access equity has been raised as a fundamental concern with virtual care, both as it was used during the SARS-CoV-2 pandemic as well as its future applications within health systems. These concerns have not yet been substantiated with quantifiable data. We conducted a comparison of healthcare utilization and access across all dimensions of the Ontario Marginalization Index between virtual care and in-person care in the province of Ontario. METHODS: We conducted a retrospective observational study using ICES databases in the Province of Ontario between March 14, 2020, and March 13,2022. We identified all virtual and in-person visits using billing codes. All visits were linked to their individual postal dissemination area for which there was census data from the Ontario Marginalization Index. Dissemination areas were divided, according to their categorization within each marginalization dimension, and visit rates were calculated for both populations. RESULTS: A total of 93,363,194 visits were included as part of the final analysis. Significant differences in virtual healthcare utilization were noted between the most and least marginalized populations within each dimension. This effect was not observed by visits for in-person care. The only exception was that racialized, and newcomer populations had higher virtual care utilization among the most marginalized. INTERPRETATION: This data is the first that uses a large retrospective dataset and seems to confirm concerns for access inequity among the most marginalized populations. These differences much be part of policy considerations for the future of virtual care use.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 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".