Improving Middle-Income Countries Access to Vaccines. A Blueprint to Overcome Current Challenges
Bibliographic record
Abstract
The Global Health Consortium at Florida International University developed an end-to-end solution framework based on the input of a diverse panel of experts from middle-income country (MIC) government agencies, public health think tanks, academia, and nonprofit organizations to identify mechanisms to help MIC governments and stakeholders increase access to novel vaccines for infectious diseases. The resultant layout can be deployed to improve vaccine discovery and development, strengthen regulatory processes, and boost vaccine production, access, and implementation. Mechanisms include policies and incentives MIC governments can use to stimulate vaccine investment and activity, as well as actions government agencies can take together with other stakeholders to coordinate efforts or build capabilities. Through a series of individual virtual interviews, we engaged diverse experts from MIC government agencies, public health think tanks, academia, and nonprofit organizations who understand the vaccine ecosystem, immunization policies, and population health financing at global, regional, and country levels. Responses were mapped, and in-depth questions were prepared for a group virtual discussion. This paper is the result of such a group discussion. The panel identified clear opportunities for MICs to improve locally-driven innovations and future access to novel vaccines. It proposes a solution framework for countries considering investing in vaccine research and development and innovation to use as a guide to evaluate the steps they could take to improve such an environment and incentivize innovation in vaccine development. It is hoped that this end-to-end solution framework will become a key resource to help MICs strengthen policies and take more actions to make such improvements.
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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 | 0.020 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".