Health worker migration from South Africa: causes, consequences and policy responses
Bibliographic record
Abstract
BACKGROUND: This paper arises from a four-country study that sought to better understand the drivers of skilled health worker migration, its consequences, and the strategies countries have employed to mitigate negative impacts. The four countries-Jamaica, India, the Philippines, and South Africa-have historically been "sources" of skilled health workers (SHWs) migrating to other countries. This paper presents the findings from South Africa. METHODS: The study began with a scoping review of the literature on health worker migration from South Africa, followed by empirical data collected from skilled health workers and stakeholders. Surveys were conducted with physicians, nurses, pharmacists, and dentists. Interviews were conducted with key informants representing educators, regulators, national and local governments, private and public sector health facilities, recruitment agencies, and professional associations and councils. Survey data were analyzed using descriptive statistics and regression models. Interview data were analyzed thematically. RESULTS: There has been an overall decrease in out-migration of skilled health workers from South Africa since the early 2000s largely attributed to a reduced need for foreign-trained skilled health workers in destination countries, limitations on recruitment, and tighter migration rules. Low levels of worker satisfaction persist, although the Occupation Specific Dispensation (OSD) policy (2007), which increased wages for health workers, has been described as critical in retaining South African nurses. Return migration was reportedly a common occurrence. The consequences attributed to SHW migration are mixed, but shortages appear to have declined. Most promising initiatives are those designed to reinforce the South African health system and undertaken within South Africa itself. CONCLUSIONS: In the near past, South Africa's health worker shortages as a result of emigration were viewed as significant and harmful. Currently, domestic policies to improve health care and the health workforce including innovations such as new skilled health worker cadres and OSD policies appear to have served to decrease SHW shortages to some extent. Decreased global demand for health workers and indications that South African SHWs primarily use migratory routes for professional development suggest that health worker shortages as a result of permanent migration no longer pertains to South Africa.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".