Why the MDGs need good governance in pharmaceutical systems to promote global health
Bibliographic record
Abstract
BACKGROUND: Corruption in the health sector can hurt health outcomes. Improving good governance can in turn help prevent health-related corruption. We understand good governance as having the following characteristics: it is consensus-oriented, accountable, transparent, responsive, equitable and inclusive, effective and efficient, follows the rule of law, is participatory and should in theory be less vulnerable to corruption. By focusing on the pharmaceutical system, we explore some of the key lessons learned from existing initiatives in good governance. As the development community begins to identify post-2015 Millennium Development Goals targets, it is essential to evaluate programs in good governance in order to build on these results and establish sustainable strategies. This discussion on the pharmaceutical system illuminates why. DISCUSSION: Considering pharmaceutical governance initiatives such as those launched by the World Bank, World Health Organization, and the Global Fund, we argue that country ownership of good governance initiatives is essential but also any initiative must include the participation of impartial stakeholders. Understanding the political context of any initiative is also vital so that potential obstacles are identified and the design of any initiative is flexible enough to make adjustments in programming as needed. Finally, the inherent challenge which all initiatives face is adequately measuring outcomes from any effort. However in fairness, determining the precise relationship between good governance and health outcomes is rarely straightforward. SUMMARY: Challenges identified in pharmaceutical governance initiatives manifest in different forms depending on the nature and structure of the initiative, but their regular occurrence and impact on population-based health demonstrates growing importance of addressing pharmaceutical governance as a key component of the post-2015 Millennium Development Goals. Specifically, these challenges need to be acknowledged and responded to with global cooperation and innovation to establish localized and evidence-based metrics for good governance to promote global pharmaceutical safety.
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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 | 0.033 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 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".