Can a multistakeholder initiative improve transparency and accountability in the pharmaceutical sector? Evidence from the Medicines Transparency Alliance
Bibliographic record
Abstract
Background Access to affordable quality essential medicines is crucial if universal health coverage and the Sustainable Development Goals are to be achieved. Multistakeholder initiatives might help to overcome barriers to access such as weak pharmaceutical sector governance and lack of transparency and public accountability. The Medicines Transparency Alliance (MeTA) is a multistakeholder initiative implemented in the pharmaceutical sectors of seven countries (Ghana, Jordan, Kyrgyzstan, Peru, the Philippines, Uganda, and Zambia) between 2008 and 2015. In this study, we aimed to assess whether MeTA has had an effect on transparency (defined here as the degree to which access to information is available and citizens are informed about how and why government decisions are made) and accountability in the pharmaceutical sector in the seven pilot countries. Methods We applied case study methods to examine MeTA's efforts to increase transparency and accountability in the seven pilot countries. We reviewed archival data that focused on MeTA Phase II (August, 2011, to December, 2015), although we also noted key events and data from Phase I. We included: country-level semi-annual progress reports, work plans, Department for International Development (DFID) annual review reports, MeTA global meeting notes and presentations, country-level technical study reports, stakeholder forum reports, country policies, and content from web sites and social media. We compared information between countries to identify commonalities and differences in strategies and tactics used to promote MeTA's goals and, where possible, identify how these strategies might link to project results, particularly with regard to improving access to medicines. Findings We found that the pilot countries used special studies and analyses, open meetings, and proactive information dissemination strategies to expand transparency. Additionally, MeTA fostered multistakeholder policy dialogue to bring together different actors to discuss evidence on access to medicines barriers and progress. We found strong evidence that transparency was improved, for example, through the promotion of proactive dissemination strategies by the government, as well as open public meetings to discuss medicines access issues. Furthermore, MeTA's efforts contributed to new policies, such as revised national medicines policies (for example, in Ghana, Kyrgyzstan, and Uganda) and the elimination of taxes on imported raw material for medicines, as happened in Ghana. Interpretation Our study provides evidence that transparency can be improved in the pharmaceutical sector through multistakeholder initiatives, and that increased availability of information, coupled with communications between the various stakeholders, could facilitate progress towards access-to-medicines goals and result in greater government accountability. Longer-term outcomes will depend on the sustainability of initiatives, which is furthered predominantly through country ownership of such programmes, and specific actions to promote transparency in the pharmaceutical sector as good practice and clarify how information is used to hold institutions and leaders accountable for performance. Funding World Health Organization through a grant from the UK Department for International Development.
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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 | 0.333 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".