Tackling Corruption in the Pharmaceutical Systems Worldwide with Courage and Conviction
Bibliographic record
Abstract
Poor drug access continues to be one of the main global health problems. Global inequalities in access to pharmaceuticals are caused by a number of variables including poverty, high drug prices, poor health infrastructure, and fraud and corruption--the latter being the subject of this article. There is growing recognition among policy makers that corruption in the pharmaceutical system can waste valuable resources allocated to pharmaceutical products and services. This, in turn, denies those most in need from life-saving or life-enhancing medicines. As a result, international organizations, including the World Health Organization and the World Bank are beginning to address the issue of corruption in the health sector broadly and the pharmaceutical system specifically. This is encouraging news for improving drug access for the global poor who are most harmed by corruption as they tend to purchase less expensive drugs from unqualified or illegal drug sellers selling counterfeit or sub-standard drugs. In our paper, we illuminate what are the core issues that relate to corruption in the pharmaceutical sector. We argue that corruption in the pharmaceutical system can be detrimental to a country's ability to improve the health of its population. Moreover, unless policy makers deal with the issue of corruption, funding allocated to the pharmaceutical system to treat health conditions may simply be wasted and the inequality between rich and poor in access to health and pharmaceutical products will be aggravated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".