Growth and development of oncology nursing in Africa
Bibliographic record
Abstract
There is a growing recognition that oncology nurses are vitally important for an effective cancer control system. Although there is variation among countries, oncology nursing is being recognized as a specialty practice and seen as a priority for development in cancer control plans in many settings. Ministries of Health in many countries are beginning to acknowledge the role nurses play in achieving successful cancer control outcomes. Additionally, the need for access to relevant education for oncology nursing practice is being recognized by nursing and policy leaders. The purpose of this paper is to highlight the growth and development of oncology nursing in Africa. Several vignettes are presented by nurse leaders in cancer care from several African countries. Their descriptions offer brief illustrations regarding the leadership nurses are providing in cancer control education, clinical practice, and research in their respective countries. The illustrations offer insight into the urgent need, and the potential, for future development of oncology nursing as a specialty given the many challenges nurses face across the African continent. The illustrations may also provide encouragement and ideas for nurses in countries where there is little current development of the specialty about how to proceed to mobilize efforts aimed toward its growth.
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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 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.004 |
| 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".