Safeguarding people living in vulnerable conditions in the COVID-19 era through universal health coverage and social protection
Bibliographic record
Abstract
The COVID-19 pandemic is unprecedented. The pandemic not only induced a public health crisis, but has led to severe economic, social, and educational crises. Across economies and societies, the distributional consequences of the pandemic have been uneven. Among groups living in vulnerable conditions, the pandemic substantially magnified the inequality gaps, with possible negative implications for these individuals' long-term physical, socioeconomic, and mental wellbeing. This Viewpoint proposes priority, programmatic, and policy recommendations that governments, resource partners, and relevant stakeholders should consider in formulating medium-term to long-term strategies for preventing the spread of COVID-19, addressing the virus's impacts, and decreasing health inequalities. The world is at a never more crucial moment, requiring collaboration and cooperation from all sectors to mitigate the inequality gaps and improve people's health and wellbeing with universal health coverage and social protection, in addition to implementation of the health in all policies approach.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".