Corruption risks in health procurement during the COVID-19 pandemic and anti-corruption, transparency and accountability (ACTA) mechanisms to reduce these risks: a rapid review
Bibliographic record
Abstract
BACKGROUND: Health systems are often susceptible to corruption risks. Corruption within health systems has been found to negatively affect the efficacy, safety, and, significantly, equitable distribution of health products. Enforcing effective anti-corruption mechanisms is important to reduce the risks of corruption but requires first an understanding of the ways in which corruption manifests. When there are public health crises, such as the COVID-19 pandemic, corruption risks can increase due to the need for accelerated rates of resource deployment that may result in the bypassing of standard operating procedures. MAIN BODY: A rapid review was conducted to examine factors that increased corruption risks during the COVID-19 pandemic as well as potential anti-corruption, transparency and accountability (ACTA) mechanisms to reduce these risks. A search was conducted including terms related to corruption, COVID-19, and health systems from January 2020 until January 2022. In addition, relevant grey literature websites were hand searched for items. A single reviewer screened the search results removing those that did not meet the inclusion criteria. This reviewer then extracted data relevant to the research objectives from the included articles. 20 academic articles and 17 grey literature pieces were included in this review. Majority of the included articles described cases of substandard and falsified products. Several papers attributed shortages of these products as a major factor for the emergence of falsified versions. Majority of described corruption instances occurred in low- and middle-income countries. The main affected products identified were chloroquine tablets, personal protective equipment, COVID-19 vaccine, and diagnostic tests. Half of the articles were able to offer potential anti-corruption strategies. CONCLUSION: Shortages of health products during the COVID-19 pandemic seemed to be associated with increased corruption risks. We found that low- and middle-income countries are particularly vulnerable to corruption during global emergencies. Lastly, there is a need for additional research on effective anti-corruption mechanisms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".