South African physician emigration and return migration, 1991–2017: a trend analysis
Bibliographic record
Abstract
Although critical for understanding health labour market trends in low- and middle-income countries (LMICs), longitudinal LMIC health worker emigration and return migration trends are not routinely documented. This article seeks to better understand SA's trends in physician emigration and return migration and whether economic growth and related policies affect migration patterns. This study used physician registry data to analyse patterns of emigration and return migration only among SA-trained physicians registered to practice in top destination countries such as Australia, Canada, New Zealand, the USA or the UK between 1991 and 2017, which represent the top five emigration destinations for this group. A linear regression model analysed the relationship between migration trends (as dependent variables) and SA's economic growth, health financing and HIV prevalence (as independent variables). There has been a 6-fold decline in emigration rates from SA between 1991 and 2017 (from 1.8% to 0.3%/year), with declines in emigration to all five destination countries. About one in three (31.8% or 5095) SA physicians returned from destination countries as of 2017. Annual physician emigration fell by 0.16% for every $100 rise in SA GDP per capita (2011 international dollars) (95% confidence interval -0.60% to -0.086%). As of 2017, 21.6% (11 224) of all SA physicians had active registration in destination nations, down from a peak of 33.5% (16 366) in 2005, a decline largely due to return migration. Changes to the UK's licensing regulations likely affected migration patterns while the Global Code of Practice on International Recruitment contributed little to changes. A country's economic growth might influence physician emigration, with significant contribution from health workforce policy interventions. Return migration monitoring should be incorporated into health workforce planning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".