Anti-corruption in global health systems: using key informant interviews to explore anti-corruption, accountability and transparency in international health organisations
Bibliographic record
Abstract
OBJECTIVES: Corruption undermines the quality of healthcare and leads to inequitable access to essential health products. WHO, Global Fund, United Nations Development Programme (UNDP) and World Bank are engaged in anti-corruption in health sectors globally. Throughout the COVID-19 pandemic, weakened health systems and overlooked regulatory processes have increased corruption risks. The objective of this study is thus to explore the strengths and weaknesses of these organisations' anti-corruption mechanisms and their trajectories since the pandemic began. DESIGN, SETTING AND PARTICIPANTS: 25 semistructured key informant interviews with a total of 27 participants were conducted via Zoom between April and July 2021 with informants from WHO, World Bank, Global Fund and UNDP, other non-governmental organisations involved in anti-corruption and academic institutions. Key informant selection was guided by purposive and snowball sampling. Detailed interview notes were qualitatively coded by three researchers. Data analysis followed an inductive-deductive hybrid thematic analysis framework. RESULTS: The findings demonstrate that WHO, World Bank, Global Fund and UNDP have shifted from criminalisation/punitive approaches to anti-corruption to preventative ones and that anti-corruption initiatives are strong when they are well funded, explicitly address corruption and are complemented by strong monitoring and evaluation mechanisms. Weaknesses in the organisations' approaches to anti-corruption include one-size-fits-all approaches, lack of political will to address corruption and zero-tolerance policies for corruption. The COVID-19 pandemic has highlighted the necessity of improving anti-corruption by promoting strong accountability and transparency in health systems. CONCLUSIONS: Results from this study highlight the strengths, weaknesses and recent trajectories of anti-corruption in the Global Fund, World Bank, UNDP and WHO. This study underscores the importance of implementing strong and robust anti-corruption mechanisms specifically geared towards corruption prevention that remain resilient even in times of emergency.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".