COVID‐19 and the Uncovering of Health Care Disparities in the United States, United Kingdom and Canada: Call to Action
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) pandemic created a crisis that disproportionately affected populations already disadvantaged with respect to access to health care systems and adequate medical care and treatments. Understanding how and where health care disparities are most widespread is an important starting point for exploring opportunities to mitigate such disparities, especially within our patient population with liver disease. In a webinar in LiverLearning, we discussed the impact of the pandemic on the United States, United Kingdom and Canada, highlighting the disproportionate effects on infection rates and death for certain ethnic minorities, those socioeconomically disadvantaged and living in higher density areas, and those working in health care and other essential jobs. We set forth a "call to action" for members of the American Association for the Study of Liver Diseases and the larger community of providers of liver disease care to generate viable solutions to improve access to care and vaccination rates of our patients against COVID-19, and in general help reduce health care disparities and improve the health of disadvantaged populations within their communities. Solutions will likely involve personalized interventions and messaging for communities that honor local leaders and embrace the diverse needs and different cultural sensitivities of our unique patient populations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".