Estimating Chinese foreign health aid: an analysis of AidData's Global Chinese Official Finance dataset
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Notice bibliographique
Résumé
BackgroundChinese global health aid has expanded tremendously since the 2000s. Unlike many donors, China has no official aid reporting obligations, nor does it voluntarily disclose detailed aid information. Therefore, several third parties have attempted to estimate China's health aid footprint. However, current estimates use varied definitions of health aid, geographic regions, and time spans. These distinct methodological approaches make comparisons of Chinese aid to other aid donors difficult. Our study builds on previous tracking efforts and improves on them by creating a standardised estimate using commonly accepted definitions of aid and frameworks for categorising health projects.MethodsWe categorised AidData's Chinese Official Finance Dataset health-related projects according to health aid frameworks from the Organization for Economic Co-operation and Development (OECD) and the Institute for Health Metrics and Evaluation (IHME). Only projects that fitted the definition of official development assistance were included. We analysed data by both total project count and financial value to assess priority health-aid focus areas for China. We also provide an updated estimate for projects with missing financial values in AidData's database by applying the median cost of similar projects to projects with missing financial values, allowing for comparison with other donors.FindingsBetween 2000 and 2014, China funded 620 health-related aid projects, which made up more than 20% of its total aid project portfolio. Most of these projects were located in Africa. According to the OECD framework, the priority focus areas of these 620 projects were: basic health care, such as medical teams, drugs, and medicine (n=244, 36%); malaria control (118, 19%); medical services, such as specialty equipment, infrastructure and services (108, 17%); and basic health infrastructure (78, 13%). According to the IHME framework, health-systems strengthening accounted for 70% (n=434) of total projects, primarily due to China's contributions to human resources for health, infrastructure, and equipment. The only other significant allocation under the IHME framework was malaria (n=118, 19%). When we estimate missing financial values, we noted that China was the fourth largest health aid donor to African countries from 2008–2014, after the USA, UK, and Canada.InterpretationThese findings enable a better understanding of Chinese health aid in the absence of transparent aid reporting. Such understanding could lead to better coordination, collaboration, and resource allocation for both fellow donors and recipient countries.FundingHuang Fellows Program, Duke University Science & Society (PK).
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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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 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 découle