The Future of Virtual Care for Older Ethnic Adults Beyond the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic has fundamentally changed how Canadians access health care. Although it is undeniable that the rapid adoption of virtual care has played a critical role in reducing viral transmission, the gap in equitable access to virtual care remains pervasive for Canada's aging and ethnocultural minority communities. Existing virtual care solutions are designed for the English-speaking, health-literate, and tech-savvy patient population, excluding older ethnic adults who often do not see themselves reflected in these identities. In acknowledging the permanency of virtual care brought on by the pandemic, we have a collective responsibility to co-design new models that serve our older ethnic patients who have been historically marginalized by the status quo. Building on existing foundations of caregiving within ethnocultural minority communities, one viable strategy to realize culturally equitable virtual care may be to engage the highly motivated and skilled family caregivers of older ethnic adults as partners in the technology-mediated management of their chronic disease. The time is now to build a model of shared virtual care that embraces Canada's diverse cultures, while also providing its older ethnic adults with access to health innovations in partnership with equally invested family caregivers who have their health at heart.
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How this classification was reachedexpand
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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".