Inequities in the Global Health Workforce: The Greatest Impediment to Health in Sub-Saharan Africa
Bibliographic record
Abstract
Health systems played a key role in the dramatic rise in global life expectancy that occurred during the 20th century, and have continued to contribute enormously to the improvement of the health of most of the world's population. The health workforce is the backbone of each health system, the lubricant that facilitates the smooth implementation of health action for sustainable socio-economic development. It has been proved beyond reasonable doubt that the density of the health workforce is directly correlated with positive health outcomes. In other words, health workers save lives and improve health. About 59 million people make up the health workforce of paid full-time health workers world-wide. However, enormous gaps remain between the potential of health systems and their actual performance, and there are far too many inequities in the distribution of health workers between countries and within countries. The Americas (mainly USA and Canada) are home to 14% of the world's population, bear only 10% of the world's disease burden, have 37% of the global health workforce and spend about 50% of the world's financial resources for health. Conversely, sub-Saharan Africa, with about 11% of the world's population bears over 24% of the global disease burden, is home to only 3% of the global health workforce, and spends less than 1% of the world's financial resources on health. In most developing countries, the health workforce is concentrated in the major towns and cities, while rural areas can only boast of about 23% and 38% of the country's doctors and nurses respectively. The imbalances exist not only in the total numbers and geographical distribution of health workers, but also in the skills mix of available health workers. WHO estimates that 57 countries world wide have a critical shortage of health workers, equivalent to a global deficit of about 2.4 million doctors, nurses and midwives. Thirty six of these countries are in sub- Saharan Africa. They would need to increase their health workforce by about 140% to achieve enough coverage for essential health interventions to make a positive difference in the health and life expectancy of their populations. The extent causes and consequences of the health workforce crisis in Sub-Saharan Africa, and the various factors that influence and are related to it are well known and described. Although there is no "magic bullet" solution to the problem, there are several documented, tested and tried best practices from various countries. The global health workforce crisis can be tackled if there is global responsibility, political will, financial commitment and public-private partnership for country-led and country-specific interventions that seek solutions beyond the health sector. Only when enough health workers can be trained, sustained and retained in sub-Saharan African countries will there be meaningful socio-economic development and the faintest hope of attaining the Millennium Development Goals in the sub-continent.
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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 | 0.004 |
| 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 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".