What Do Core Obligations under the Right to Health Bring to Universal Health Coverage?
Bibliographic record
Abstract
Can the right to health, and particularly the core obligations of states specified under this right, assist in formulating and implementing universal health coverage (UHC), now included in the post-2015 Sustainable Development Goals? In this paper, we examine how core obligations under the right to health could lead to a version of UHC that is likely to advance equity and rights. We first address the affinity between the right to health and UHC as evinced through changing definitions of UHC and the health domains that UHC explicitly covers. We then engage with relevant interpretations of the right to health, including core obligations. We turn to analyze what core obligations might bring to UHC, particularly in defining what and who is covered. Finally, we acknowledge some of the risks associated with both UHC and core obligations and consider potential avenues for mitigating these risks.
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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 | 0.027 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.027 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".