Increasing transparency and accountability in national pharmaceutical systems
Bibliographic record
Abstract
Access to safe, effective, good-quality medicines can be compromised by poor pharmaceutical system governance. This system is particularly vulnerable to inefficiencies and to losses from corruption, because it involves a complex mix of actors with diverse responsibilities. A high level of transparency and accountability is critical for minimizing opportunities for fraud and leakage. In the past decade, the Good Governance for Medicines programme and the Medicines Transparency Alliance focused on improving accountability in the pharmaceutical system and on reducing its vulnerability to corruption by increasing transparency and encouraging participation by a range of stakeholders. Experience with these two programmes revealed that stakeholders interpreted transparency and accountability in a range of different ways. Moreover, programme implementation and progress assessments were complicated by a lack of clarity about what information should be disclosed by governments and about how greater transparency can strengthen accountability for access to medicines. This article provides a conceptual understanding of how transparency can facilitate accountability for better access to medicines. We identified three categories of information as prerequisites for accountability: (i) standards and commitments; (ii) decisions and results; and (iii) consequences and responsive actions. Examples are provided for each. Conceptual clarity and practical examples of the information needed to ensure accountability can help policy-makers identify the actions required to increase transparency and accountability in their pharmaceutical systems. We also discuss factors that can hinder or facilitate the use of information to hold to account those responsible for improving access to medicines.
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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 | 0.039 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".