The Rise of Virtual Care in the Pandemic Era: Ensuring Equitable Systems for Our Most Marginalized Populations
Bibliographic record
Abstract
To the Editor: Early in 2020, U.S. health care systems with large-scale virtual care integration were a minority, with little incentive and many barriers to change. 1 Although virtual care has been gaining traction for years, it went from being a rarity to a standard during the COVID-19 pandemic. COVID-19 disproportionately impacts marginalized communities, 2 and unfortunately, the pivot toward virtual care carries this same concern. Though there is great utility to virtual care, without an equity-based approach, it may pose unique challenges for our most vulnerable populations. Among the many barriers that virtual care poses are: (1) limited access to technology, (2) challenges with digital literacy, (3) language differences without professional interpretation, (4) impaired access to self-monitoring medical equipment, and (5) lack of space to ensure privacy and confidentiality. Without intentional strategies to mitigate these barriers, we risk exacerbating health disparities for already marginalized patients who may not be able to advocate an in-person alternative. During this formative time in health care delivery, there are powerful opportunities to design virtual care systems that are equitable. The development of various interventions has demonstrated great promise for greater inclusivity. For example, various North American telecom companies have devised programs providing free devices plans for patients with low socioeconomic status. 3 While working on primary care teams as clinical clerks, we recognized the importance of highlighting these programs for patients who need them. We created an information sheet with the contact information of various community organizations willing to donate virtual care resources. We also provided various patients with over-the-phone and in-person tutorials on how to access local virtual care platforms. Often as medical students, we are taught the importance of advocacy, and now this is more apparent than ever. Medical students should be encouraged to work with health professionals, community members, and policy makers to ensure that the sudden shifts in the health care systems benefit everyone, leaving no one behind. Acknowledgments: The authors would like to thank Dr. Eileen Nicolle for her support in providing improved access to virtual care patients at Sumac Creek Health Centre, St. Michael’s Hospital, Toronto, Ontario, Canada.
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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 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.017 | 0.027 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".