The Future of Virtual Care for Older Ethnic Adults Beyond the COVID-19 Pandemic (Preprint)
Bibliographic record
Abstract
UNSTRUCTURED 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 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".