MétaCan
Menu
Back to cohort
Record W2102996639 · doi:10.2174/1874220301401010017

Migration of International Medical Graduates: Implications for the Brain-Drain

2015· article· en· W2102996639 on OpenAlexaboutno aff
O.C. Nwagwu Emeka

Bibliographic record

VenueOpen Medicine Journal · 2015
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsBrain drainPovertyEmigrationEconomic shortageHealth careEconomic growthDeveloping countryDevelopment economicsPolitical scienceMedicineEconomicsLaw

Abstract

fetched live from OpenAlex

Studies indicate that about 23 percent to 28 percent of the physicians working and residing in the United States, Canada, Australia, the UK and New Zealand were born and trained in the low-income countries, areas suffering from critical shortages of physicians and other health workers. In the US alone, the preponderance of the foreign physicians hails from South Africa, Philippines, India, Pakistan, and Nigeria. From Africa alone where the burden of disease, poverty, deprivation and death are greatest, around 23,000 qualified physicians emigrate annually. From the perspectives of the low-income countries, significant amounts of resources are, by necessity, committed into turning their nationals into vital intellectual capital for their own desperately needed health needs and crumbling healthcare systems. Thus, the migration of these physicians to other nations to help strengthen their already stable health care systems is not only ethically deplorable but poses moral hazards for both the physicians and the high-income countries. That is, high-income countries such as the United States, Canada, UK, Australia and New Zealand are draining the scarce recourses of the low-income countries through the loss of intellectual capital, a phenomenon that socio-economic and developmental experts have dubbed “the brain drain”.

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)0.007
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.856
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.219
GPT teacher head0.566
Teacher spread0.347 · 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; a candidate call from one teacher head, not a consensus.

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".

Quick stats

Citations10
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueOpen Medicine JournalSame topicGlobal Health Workforce IssuesFrench-language works237,207