Bibliographic record
Abstract
UNSTRUCTURED The critical intersections of structural inequities and vulnerabilities of marginalized populations, particularly those engaging the social gradient of minority ethnic communities, are revealed in the syndemic approach to COVID-19. Although proposals for cultural interventions to improve virtual care provide relevant measures, they may not address the root cause of the disparate impacts of a pandemic on population subgroups. The common misperception of equality as synonymous with equity further impedes the efficacy of digital health in quality-of-care initiatives, as it systemically fails to acknowledge the disparate realities of marginalized populations, while intending to benefit all. This commentary suggests that an alignment of the health care system with Canada’s pluralist principles would support a paradigm shift in transforming virtual care into an equitable standard as envisioned by Pham and colleagues in their paper, “The Future of Virtual Care for Older Ethnic Adults Beyond the COVID-19 Pandemic.”
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".