Bibliographic record
Abstract
The rise of the novel coronavirus disease 2019 (COVID-19) caused unprecedented public health responses worldwide. To prevent hospitals from oversaturating, nations are restructuring their healthcare systems to prioritize limited resources and care for the treatment of COVID-19-infected patients. The Italian healthcare system, for example, converted numerous hospital services to Intensive Care Units, redeployed physicians to short-staffed centers, and centralized medical services to a small number of hospitals to meet the pandemic’s demands. While this restructuring served the nation’s short-term healthcare needs, it impeded access to care for non-COVID-19 patients suffering from acute or chronic non-communicable diseases, such as strokes. These patients are at increased risk of long-term disability and poorer adherence to management plans and have an increased likelihood of disease recurrence. This commentary discusses the ethical dilemma surrounding the necessary healthcare restructuring and unintended impairment of care to non-infected patients. It also explores the need for national public health officials to reassess strategies employed during the pandemic and their need to focus on creating ethical frameworks for maximizing equitable care.
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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".