Cancer care workforce in Africa: perspectives from a global survey
Bibliographic record
Abstract
While the burden of cancer in Africa is rapidly rising, there is a lack of investment in healthcare professionals to deliver care. Here we report the results of a survey of systemic therapy workload of oncologists in Africa in comparison to oncologists in other countries. An online survey was distributed through a snowball method via national oncology societies to chemotherapy-prescribing physicians in 65 countries. The survey was distributed within Africa through a network of physicians associated with the African Organisation for Research and Training in Cancer (AORTIC). Workload was measured as the annual number of new cancer patient consults seen per oncologist. Job satisfaction was ranked on a 10-point Likert scale; scores of 9–10 were considered to represent high job satisfaction. Thirty-six oncologists from 18 countries in Africa and 1079 oncologists from 47 other countries completed the survey. Compared to oncologists from other countries, African oncologists were older (median age 51 vs 44 years, p = 0.007), more likely to prescribe chemotherapy and radiation [61% (22/36) vs 10% (108/1079), p < 0.001], less likely to have completed training in their home country [50% (18/36) vs 91% (979/1079), p < 0.001], and more likely to work in the private sector [47% (17/36) vs 34% (364/1079), p = 0.037]. The median number of annual consults per oncologist was 325 in Africa compared to175 in other countries. The proportion of oncologists seeing > 500 consults/year was 31% (11/36) in Africa compared to 12% (129/1079) in other countries ( p = 0.001). African oncologists were more likely than global colleagues to see all cancer sites [72% (26/26) vs 24% (261/1079), p < 0.001]. Oncologists in Africa were less likely than other oncologists to have high job satisfaction [17% (6/36) vs 30% (314/1079), p = 0.013]. African oncologists within the AORTIC network have a substantially higher clinical workload and lower job satisfaction than oncologists elsewhere in the world. There is an urgent need for governments and health systems to improve the oncologist-to-patient ratio and develop new models of capacity building, retention and skills enhancement to strengthen the wide variety of cancer care systems across continental 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".