Strengthening of oncology nursing education and training in Africa in the year of the nurse and midwife: addressing the challenges to improve cancer control in Africa
Bibliographic record
Abstract
The Cancer burden in Africa is increasing. Nurses play a pivotal role in health care systems and find themselves in a key position to engage with patients, communities and other health professionals to address disparities in cancer care and work towards achieving cancer control in Africa. The rapidly evolving nature of cancer care requires a highly skilled and specialised oncology nurse to either provide clinical care and/or conduct research to improve evidence-based practice. Although Africa has been slow to respond to the need for trained oncology nurses, much has been done over the past few years. This article aims to provide an update of Oncology nursing education and training in Africa with specific focus on South Africa, Ghana, Nigeria, Kenya, Zambia and Egypt. Mapping oncology nursing education and training in Africa in 2020, the International Year of the Nurse and the Midwife, provides an opportunity to leverage on the essential roles of the oncology nurse and commit to an agenda that will drive and sustain progress to 2030 and beyond.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".