Shortage of healthcare workers in sub-Saharan Africa: a nephrological perspective
Bibliographic record
Abstract
AIMS: The paper assesses the lack of healthcare workers, the consequences, and possible solutions. MATERIALS AND METHODS: Review of existing literature and global health reports. RESULTS: The 47 countries of sub-Saharan Africa have a critical shortage of healthcare workers, the deficit amounting to 2.4 million doctors and nurses. There are 2 doctors and 11 nursing/midwifery personnel per 10,000 population, compared with 19 doctors and 49 nursing/midwifery personnel per 10,000 for the Americas, and 32 doctors and 78 nursing/midwifery personnel per 10,000 for Europe. And, whereas there are 28 doctors and 87 nurses/midwifery personnel per 10,000 in high income regions of the world, there are only 5 doctors and 11 nurses/ midwifery personnel per 10,000 in low income regions. The shortage of nephrologists in Africa, and especially sub-Saharan Africa, remains a critical issue, with many countries having < 1 nephrologist per million population; some have no nephrologists at all. The USA, UK, Canada and Australia have benefitted considerably from the migration of nurses and doctors over the past half century. Opportunities for training as well as employment have attracted doctors from many countries to these developed countries. Since 2006, new legislation in the UK has limited the inflow of health workers. Developing countries are also beginning to take steps to mitigate the problem of health worker loss and are developing strategies to both train increasing numbers of different cadres of healthcare worker and also to retain those already working in these countries. CONCLUSIONS: The forces of globalization are tending to increase the worldwide movement of all types of professionals, including those working in health care. It is this lack of health workers in developing countries that has been such a major constraint in limiting progress on initiatives such as the HIV "3 by 5" and Millennium Development Goals. More specifically, lack of resources, both human and financial, in developing countries has hampered nephrology programs both in the detection and prevention of chronic kidney disease and in the ability of doctors, nurses and other nephrological personnel to provide acute/chronic dialysis and transplantation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".