An exploration of anti-corruption and health in international organizations
Bibliographic record
Abstract
Corruption is a global wicked problem that threatens the achievement of health, social and economic development goals, including Sustainable Development Goal # 3: Ensuring healthy lives and promoting well-being for all. The COVID-19 pandemic and its resulting strain on health systems has heightened risks of corruption both generally and specifically within health systems. Over the past years, international organizations, including those instrumental to the global COVID-19 response, have increased efforts to address corruption within their operations and related programs. However, as attention to anti-corruption efforts is relatively recent within international organizations, there is a lack of literature examining how these organizations address corruption and the impact of their anti-corruption efforts. This study addresses this gap by examining how accountability, transparency, and anti-corruption are taken up by international organizations within their own operations and the reported outcomes of such efforts. The following international organizations were selected as the focus of this document analysis: the World Health Organization, the Global Fund, the United Nations Development Programme, and the World Bank Group. Documents were identified through a targeted search of each organization's website. Documents were then analyzed combining elements of content analysis and thematic analysis. The findings demonstrate that accountability and transparency mechanisms have been employed by each of the four international organizations to address corruption. Further, these organizations commonly employed oversight mechanisms, including risk assessments, investigations, and audits to monitor their internal and external operations for fraud and corruption. All organizations used sanction strategies meant to reprimand identified transgressors and deter future corruption. Findings also demonstrate a marked increase in anti-corruption efforts by these international organizations in recent years. Though this is promising, there remains a distinct absence of evidence demonstrating the impact of such efforts on the prevalence and severity of corruption in international organizations.
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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 | 0.029 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.013 | 0.022 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".