Growth and development of oncology nursing in Asia
Bibliographic record
Abstract
Oncology nursing is increasingly recognized around the world as being vitally important for an effective cancer control system. Granted, there is variation between and among countries/regions regarding the strength and nature of that recognition, but oncology nursing is clearly seen as a specialty practice and as a priority for development in cancer control plans, especially for high resource countries/regions. Many countries/regions are beginning to recognize that nurses are vitally important to their cancer control efforts and nurses require specialized education and infrastructure support to make a substantial contribution. The purpose of this paper is to highlight the growth and development of cancer nursing in Asia. Several brief summaries are presented by nurse leaders in cancer care from several Asian countries/regions. Their descriptions reflect illustrations of the leadership nurses are providing in cancer control practice, education, and research in their respective countries/regions. The illustrations also reflect the potential for future development and growth of oncology nursing as a specialty given the many challenges nurses face across Asia. The development of relevant education programs following basic nursing preparation, the establishment of specialty organizations for oncology nurses, and engagement by nurses in policy activity have been influential factors in the growth of oncology nursing in Asia.
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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 | 0.003 | 0.002 |
| 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 | 0.003 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".