Assessing 15 Proposals for Promoting Innovation and Access to Medicines Globally
Bibliographic record
Abstract
BACKGROUND: There is widespread recognition that the existing global systems for innovation and access to medicines need reform. Billions of people do not have access to the medicines they need, and market failures prevent new drugs from being developed for diseases that primarily affect the global poor. The World Health Organization's Consultative Expert Working Group on Research and Development: Financing and Coordination (CEWG) analyzed numerous proposals for reform. The aim of this article is to build on these previous inquiries. METHODS: We conducted a structured analysis that grouped proposals into five broad opportunities for global policy reform to help researchers and decision makers to meaningfully evaluate each proposal in comparison with similar proposals. Proposals were also analyzed along three important dimensions-potential health impact, financial implications, and political feasibility-further facilitating the comparison and application of this information. FINDINGS: Upon analysis, no one solution was deemed a panacea, as many (often competing) considerations need to be taken into account. However, some proposals, particularly product development partnership and prizes, appeared more promising and feasible at this time and deserve further attention. CONCLUSION: More research is needed into the effectiveness of these mechanisms and their transferability across jurisdictions.
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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 | 0.130 | 0.186 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.030 | 0.024 |
| Open science | 0.006 | 0.028 |
| Research integrity | 0.023 | 0.009 |
| Insufficient payload (model declined to judge) | 0.064 | 0.007 |
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".