Corruption risks in COVID-19 vaccine deployment: lessons learned for future pandemic preparedness
Notice bibliographique
Résumé
BACKGROUND: During the COVID-19 pandemic corruption risks were amplified in health systems globally, increasing health inequities within and between countries. During the pandemic, the deployment of COVID-19 vaccines, particularly concerning their procurement and distribution, had corruption risks given the large amounts of public and private funding allocated to them, the need for speed, the involvement of a high number of stakeholders, and often insufficient oversight. To explore this issue further, we conducted a descriptive, qualitative study of corruption risks in the COVID-19 vaccine deployment process. METHODS: We conducted a descriptive, qualitative study triangulating two data sources between May and August 2022: (1) published academic and grey literature and (2) key informant interviews with representatives from organizations involved with the COVAX Facility, representatives from COVAX donor and recipient countries, and individuals with expert knowledge of the COVID-19 vaccine deployment process (e.g., consultants for international organizations involved in COVID-19 vaccine deployment, members of non-governmental organizations, etc.). RESULTS: We identified 44 academic articles and policy documents and triangulated. Documentary data with 16 key informant interviews. A review of the literature identified several corruption risks in the international COVID-19 vaccine procurement and distribution process such as a lack of transparency in the vaccine procurement process; a lack of transparency in the operation of the COVAX Facility; a risk of bribery; and a risk of vaccine theft or the introduction of substandard and falsified vaccines at the point of distribution. Key informants further articulated concerns about a lack of transparency in vaccine pricing and contracts and the exclusion of civil society organizations from the vaccine deployment process. Reported anti-corruption, transparency, and accountability (ACTA) mechanisms implemented across the many levels of the vaccine procurement and distribution deployment included institutional oversight processes, blockchain-based supply-chain solutions, and civil society engagements. CONCLUSION: Public health emergencies require nimble and quick actions on the part of governments, international organizations and other actors Our study on the COVID-19 vaccine deployment process highlights the pressing need for more robust ACTA mechanisms to reduce corruption risks and ensure fair and equitable access to lifesaving vaccines for populations.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».