The risk of corruption in public pharmaceutical procurement: how anti-corruption, transparency and accountability measures may reduce this risk
Bibliographic record
Abstract
Background: The goal of the public procurement of pharmaceuticals is to purchase sufficient quantities of high-quality pharmaceuticals at cost-effective prices for a given population. This goal can be undercut if corruption infiltrates the procurement process. Good procurement practices can help mitigate the risks of corruption and support equitable access to affordable and high-quality medicines.Objectives: This paper aims to 1) examine manifestations of corruption in the pharmaceutical procurement process and key factors behind them, and 2) identify how to design and implement effective anti-corruption, transparency and accountability mechanisms within this process.Methods: This paper was informed by a narrative literature review from 1996 to the present. The search focused on publications that addressed the issue of pharmaceutical procurement and governance and corruption issues. Our search included peer-reviewed literature, books, grey literature such as working papers, reports published by international organizations and donor agencies, and some media articles. Some documents used in this paper were already known to the authors.Results: Procurement is highly vulnerable to corruption particularly in the health sector. What is more, corruption in the procurement process does not appear to be limited to any one level of government or type of health system. The better integration of accountability, transparency and anti-corruption mechanisms in the procurement process is needed to reduce the risk of corruption.Conclusions: Lessons learned suggest that anti-corruption, transparency and accountability mechanisms in the pharmaceutical procurement process, such as open contracting and integrity pacts are helpful towards reducing the risk of corruption.
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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 | 0.031 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".