Global health security and universal health coverage: from a marriage of convenience to a strategic, effective partnership
Bibliographic record
Abstract
Global health security and universal health coverage have been frequently considered as "two sides of the same coin". Yet, greater analysis is required as to whether and where these two ideals converge, and what important differences exist. A consequence of ignoring their individual characteristics is to distort global and local health priorities in an effort to streamline policymaking and funding activities. This paper examines the areas of convergence and divergence between global health security and universal health coverage, both conceptually and empirically. We consider analytical concepts of risk and human rights as fundamental to both goals, but also identify differences in priorities between the two ideals. We support the argument that the process of health system strengthening provides the most promising mechanism of benefiting both goals.
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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 | 0.031 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.070 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.001 | 0.030 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".