Pandemic preparedness and response: exploring the role of universal health coverage within the global health security architecture
Bibliographic record
Abstract
In response to the COVID-19 pandemic, several international initiatives have been developed to strengthen and reform the global architecture for pandemic preparedness and response, including proposals for a pandemic treaty, a Pandemic Fund, and mechanisms for equitable access to medical countermeasures. These initiatives seek to make use of crucial lessons gleaned from the ongoing pandemic by addressing gaps in health security and traditional public health functions. However, there has been insufficient consideration of the vital role of universal health coverage in sustainably mitigating outbreaks, and the importance of robust primary health care in equitably and efficiently safeguarding communities from future health threats. The international community should not repeat the mistakes of past health security efforts that ultimately contributed to the rapid spread of the COVID-19 pandemic and disproportionately affected vulnerable and marginalised populations, especially by overlooking the importance of coherent, multisectoral health systems. This Health Policy paper outlines major (although often neglected) gaps in pandemic preparedness and response, which are applicable to broader health emergency preparedness and response efforts, and identifies opportunities to reconceptualise health security by scaling up universal health coverage. We then offer a comprehensive set of recommendations to help inform the development of key pandemic preparedness and response proposals across three themes-governance, financing, and supporting initiatives. By identifying approaches that simultaneously strengthen health systems through global health security and universal health coverage, we aim to provide tangible solutions that equitably meet the needs of all communities while ensuring resilience to future pandemic threats.
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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 | 0.018 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.013 | 0.024 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".