“Doctor Zoom will see you now”: An equity-focused perspective on virtual care in the era of COVID-19
Bibliographic record
Abstract
The COVID-19 pandemic has dramatically impacted populations and healthcare systems worldwide, especially in regions deemed “hotspots.” Social determinants of health have now become even more evident amidst the circumstances imposed by the pandemic, as traditionally underserved and marginalized populations are disproportionately impacted.1,2 In the past decade, virtual care has been proposed as a means of improving access to care for patients and can be administered across various modalities such as telephone, asynchronous messaging (email or text), videoconferencing (e.g. Ontario Telemedicine Network, Zoom), and other secure platforms (e.g. Doxy.me).3 The abrupt shutdowns imposed by the COVID-19 pandemic have accelerated the transition to virtual care across the world.3 Recently published papers have primarily focused on the global impact of virtual care or nation-specific healthcare systems.4,5 Our commentary offers an equity-focused perspective on the landscape of virtual care during the COVID-19 pandemic with an emphasis on acknowledging and addressing factors unique to the Canadian healthcare system. Specifically, we will discuss benefits of virtual care and explore the challenges imposed by the rapid conversion to virtual care in the context of social and other determinants of health in Ontario. © 2021, University of Toronto. All rights reserved.
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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 | 0.018 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.046 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.022 | 0.028 |
| Insufficient payload (model declined to judge) | 0.005 | 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".