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Record W2082914558 · doi:10.1097/qad.0b013e32835857d4

Human capital contracts for global health

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

Bibliographic record

VenueAIDS · 2012
Typeletter
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsHealth careMedicineEmigrationGlobal healthPopulationDeveloping countryEconomic growthEconomic shortageEnvironmental healthGeographyGovernment (linguistics)Economics

Abstract

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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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.018
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0010.002

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.

Opus teacher head0.067
GPT teacher head0.473
Teacher spread0.406 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreCommentary

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".

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Citations2
Published2012
Admission routes1
Has abstractyes

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