Rethinking and strengthening the Global Health Diplomacy through triangulated nexus between policy makers, scientists and the community in light of COVID-19 global crisis
Bibliographic record
Abstract
The COVID-19 pandemic is considerably the biggest global health challenge of this modern era. Spreading across all regions of the world, this corona virus disease has disrupted even some of the most advanced economies and healthcare systems. With an increasing global death toll and no near end in sight, questions on the efficacy of global response mechanisms, including the role and relevancy of global health institutions, have emerged. Using a reflexive content analytic approach, this study sheds light on some of these questions, underscoring the disconnect between science, policymaking, and society. Global health funding approaches; politicization of the pandemic, including political blame gaming; mistrust of government and other institutions; and a lack of robust accountability measures are some of the pandemic response obstacles. However, COVID-19 has also presented an opportunity for a collaboration that may potentially solidify global solidarity. A pandemic response built on strategic global health diplomacy, vaccine diplomacy, and science diplomacy can spur both political and economic benefits, advancing development, health security, and justice. The virus thrives and flourishes in face of political divisions and lack of cooperation. While the current global crisis has exacerbated the existing social injustices in societies, national unity and global solidarity is essential to winning the fight against the COVID-19 pandemic.
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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 | 0.149 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.018 | 0.062 |
| Scholarly communication | 0.038 | 0.035 |
| Open science | 0.003 | 0.032 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 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".