MétaCan
Menu
Retour à la cohorte
Enregistrement W2082914558 · doi:10.1097/qad.0b013e32835857d4

Human capital contracts for global health

2012· letter· en· W2082914558 sur OpenAlexaboutno aff
A. Hari Reddi, Andreas Thyssen, Daniel W. Smith, Jill H. Lange, Chitra Akileswaran

Notice bibliographique

RevueAIDS · 2012
Typeletter
Langueen
DomaineHealth Professions
ThématiqueGlobal Health Workforce Issues
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésHealth careMedicineEmigrationGlobal healthPopulationDeveloping countryEconomic growthEconomic shortageEnvironmental healthGeographyGovernment (linguistics)Economics

Résumé

récupéré en direct d'OpenAlex

Africa has 24% of the global disease burden, yet only 3% of the world's healthcare professionals [1]. The shortage of healthcare professionals in sub-Saharan Africa contributes to the weak domestic healthcare systems and impedes the achievement of the millennium development goals, such as reducing maternal and child mortality, treating noncommunicable chronic diseases or the eradication of pediatric HIV [2–4]. When using HIV prevalence as a proxy to signify burden of disease, it is blatant that there are an insufficient number of physicians trained per year to adequately meet the healthcare needs of the 10 sub-Saharan African countries afflicted most by HIV/AIDS (Table 1) [1].Table 1: Total physicians (per 100 000 population) and estimated lost investment in the 10 African Countries with the highest HIV/AIDS prevalence.One cause for this physician shortage is the emigration of well trained African physicians, a phenomenon popularly known as the healthcare ‘brain drain’ [5]. In a seminal economic analysis, Mills et al.[6] estimates that US$ 2.17 billion was lost by nine African countries in training physicians who then emigrated to Australia, Canada, the UK, and the USA (Table 1). Notably, the UK benefitted from the emigration of African healthcare workers by an estimated US$ 2.7 billion and the USA benefitted by US$ 846 million [6]. In an attempt to minimize this sink-source phenomenon, the World Health Assembly in 2010 adopted the Global Code of Practice on the International Recruitment of Health Personnel [7]. The resolution is a multilateral, voluntary framework that addresses the shortage of global health personnel by focusing on the migration of healthcare workers from resource-limited countries [7]. The code also calls on wealthy countries to provide financial assistance to source countries afflicted by the loss of qualified health workers [7]. We propose a solution to mitigate the healthcare brain drain by using a strategy known as human capital contracts (HCC) (first proposed by the Nobel Prize economist Milton Friedman) [8]. It works like this: an investor, such as a donor nation or global health initiative, covers the entire cost of a student's medical training [9]. In exchange, the student will work for the first 10 years of their medical career in a government or NGO sponsored health clinic in their respective country of medical education. Their medical license will be contingent on this obligatory national service. A multilateral ‘binding’ agreement between the African country and destination countries (i.e., Australia, Canada, the UK, and the USA) could prevent migration during the term period. For example, in Malawi, the College of Medicine (COM) (the country's only medical school) has graduated 372 students since 1991 [10]. Currently, the school anticipates 60 graduates per year with the intention to scale-up to 100 graduates per year [10]. The Malawian government subsidizes nearly 100% of students’ medical education, currently estimated to be US$ 32 952 per year [6]. In the case of Malawi, assuming a donor aims to triple the number of COM graduates from 60 to 180 students per year, it would cost an estimated US$ 6 million per year. Ironically, in order to tackle the physician shortage in Malawi, the United Nations Development Program (UNDP) is paying US$ 40 000 per year to attract foreign doctors [11]. It makes more sense for the UNDP to instead invest this aid into training Malawian physicians by way of a HCC. The benefits of training Malawian physicians, with stronger ties to their country, instead of importing foreign doctors are self-evident [12]. Our proposal has many advantages but we also acknowledge potential limitations. Without a concurrent increase in infrastructure capacity, African medical schools may not have the optimal environments for the increased class size. However, experience from Malawi and other African nations demonstrates that international partnerships with donors can improve medical school facilities by subsidizing construction of lecture halls, libraries, and computer labs [13]. In fact, The President Emergency Plan for AIDS Relief, through the creation of the Medical Education Partnership Initiative, committed US$ 130 million with the goal to train and support the retention of at least 140 000 new healthcare workers in Africa and included grants for medical school infrastructure development [1]. Another important consideration is the need to address quality of education. We propose coupling HCC with a mechanism of accreditation to ensure academic standards. Finally, donors of HCC will need to consider mechanisms to prevent increases in tuition (that surpass inflation) as medical schools may see this as an opportunity to increase revenue. Improving health equity vis-à-vis increasing access to healthcare is a well established intervention to achieve poverty reduction and attaining universal human rights [14]. Direct investment in medical education is an effective and well defined sector-wide approach to increase healthcare and public health capacity in Africa [15]. Financial support through the use of HCC could mitigate the ethical and economic consequences of emigration of African doctors, thereby stemming the healthcare brain drain. Acknowledgements Conflicts of interest All authors approve this manuscript. There are no conflicts of interest.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies, Intégrité de la recherche, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesIntégrité de la recherche, Charge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,018
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0020,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0020,003
Charge utile insuffisante (le modèle a refusé de juger)0,0010,002

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.

Tête enseignante Opus0,067
Tête enseignante GPT0,473
Écart entre enseignants0,406 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2012
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueAIDSMême sujetGlobal Health Workforce IssuesTravaux en français237 207