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 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 | 0.063 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| 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".