MétaCan
Menu
Back to cohort
Record W2922503080 · doi:10.1016/s2214-109x(19)30103-2

South African physician emigration, return migration, and shared migration, 1991–2017: a registry-based analysis

2019· article· en· W2922503080 on OpenAlexaboutno aff
Joseph Nwadiuko, Ligia Paina

Bibliographic record

VenueThe Lancet Global Health · 2019
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsEmigrationMedicineWorkforceAttritionFamily medicineGeographyDemographyPolitical scienceSociologyLaw

Abstract

fetched live from OpenAlex

Background Physician emigration—the so-called brain drain—is a topic of great concern to health workforce planners. South Africa has a high number of physician emigrants, yet there are few registry-based studies that have analysed whether these physicians remain abroad permanently. In this study, we aimed to use a registry-based approach to analyse return migration and shared migration in South African physicians. Methods We extracted information from registry records about South African trained physicians who had registered in Australia, Canada, New Zealand, the USA, or the UK (ACNUU nations) between 1991 and 2017. We used an attrition-based model to identify non-retiree dropouts from UK, New Zealand, and US registries and cross-referenced with a South African physician registry to determine return migration from these countries. Return migration counts in Canada were derived from secondary data from Canadian Medical Association surveys. Physicians who had active simultaneous registration in South Africa and one other nation were counted as shared migrants. Findings As of December, 2017, there were 11 224 South African trained physicians actively registered to practise in ACNUU nations, around a third (30·2%) fewer than the peak of 16 095 in 2003. Of these, 3596 (32·0%) held dual active registration (shared migration) in an ACNUU nation and South Africa. In 2017, 5008 physicians met criteria for return migration—that is, 30·1% of all non-retired South African physicians with a history of practising medicine in ACNUU countries. There has been a five-fold drop in emigration rates from South Africa between 1991 and 2017 (from 1·8% to 0·3% per year; UK −88·9%, non-UK −74·2%), negatively correlated with a substantial rise in GDP per capita growth within South Africa in time-adjusted models (–0·36% per $100 increase [p=0·01; 95% CI −0·089 to −0·632]). We noted a temporary increase in return migration between 1995 and 2009 and a simultaneous sustained increase in shared migration. Interpretation A registry-based approach allows simultaneous assessment of physician emigration, return migration, and shared migration. Our findings of decreasing rates of emigration and increasing return and shared migration rates suggest a mobility transition in medical migration in South Africa. Funding World Bank.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.020
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.008
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.042
GPT teacher head0.398
Teacher spread0.356 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueThe Lancet Global HealthSame topicGlobal Health Workforce IssuesFrench-language works237,207