The Global Health Security Index and Its Role in Shaping National COVID‑19 Response Capacities: A Scoping Review
Bibliographic record
Abstract
Introduction: Following the introduction of the Global Health Security Index (GHSI), the coronavirus disease 2019 (COVID‑19) pandemic emerged as an unprecedented global health crisis, underscoring the need for robust health security frameworks and preparedness measures. This study conducts a scoping review to analyze the existing literature on the GHSI and assess national COVID‑19 responses across different countries. Method: A comprehensive search of electronic databases (EBSCO, EMBASE, PubMed, Scopus, and Web of Science) was conducted for articles published from 2020 to 2024. Search terms included “Global Health Security Index” and terms related to COVID‑19. The study followed the Preferred Reporting Items for Systematic Reviews and Meta‑analyses for Scoping Reviews (PRISMA‑ScR) guidelines. The Newcastle–Ottawa Scale (NOS), adjusted for cross‑sectional studies, was used for quality assessment. Results: A total of 3,243 studies were identified, of which 20 were finalized for data synthesis. Specific COVID‑19 parameters were analyzed to provide a comprehensive overview of each country’s pandemic response capacity. Among the selected studies, 17 (85%) had a low risk of bias, while 3 (15%) had a medium risk. Countries’ response capacities were categorized into five key parameters: detection, mortality, transmission, fatality, and recovery. Findings revealed significant discrepancies between GHSI scores and actual national responses, with some high‑scoring countries struggling to control the pandemic. This raises concerns about the GHSI’s predictive reliability. Conclusion: The study highlights that the GHSI does not fully capture a country’s capacity to respond effectively to COVID‑19. However, it remains a valuable tool for identifying gaps in pandemic preparedness. To enhance its relevance, the index should integrate a wider range of factors, including political leadership, governance, public health infrastructure, and socio‑cultural elements, which are crucial in managing public health emergencies.
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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 | 0.051 | 0.188 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.022 | 0.019 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| 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".