Interdependent Determinants of Health and Death? Examining the Linkages between Health Equity, Human Rights, and Democracy during COVID-19
Bibliographic record
Abstract
Background: The COVID-19 pandemic has been characterised by health inequities in differential rates of COVID-19-related morbidity and mortality and differential access to essential COVID-19-related health care interventions such as vaccines. Inequities through the pandemic have deeply illuminated the interdependence between health inequities, human rights, and democratic leadership and the imperative to delve more deeply into these key determinants of health, illness, and death. Methods: In this paper, we consider what COVID-19 suggests we should be learning about the relationships between democracy, human rights, and health equity. We first elaborate on the growing prominence of the framework and discourse of health equity. We turn to elaborate on a longer-standing trend of democratic backsliding and populist leadership during COVID-19. We consider human rights violations and domestic and global inequities that have characterised COVID-19 and COVID responses. Findings and conclusions: The pandemic has illustrated how rights-violating, negligent, and inequitable political leadership can deeply determine health outcomes. It has equally shown how democratic norms and institutions, including human rights and equity, offer discourse, standards, and tools that can be effectively used to challenge inequitable leadership on health. More fundamentally, it underscores how great the need is for approaches to public health emergencies rooted in human rights, equity, and good governance, including through a pandemic treaty in negotiation.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".