The various roles of oncology nurse specialists: An international perspective
Bibliographic record
Abstract
Ilana Kadmon, PhD, RN was for many years a nurse specialist in Breast Cancer at Hadassah Medical Center, Jerusalem, Israel. From 2016, she started the role of a nurse academic consultant at the nursing division at Hadassah. Her PhD is from The University of Edinburgh, UK. Her research involved psychosocial aspects of breast cancer, and the role of the breast care nurse (BCN). She was a pioneer in developing the post of the BCN in Israel. This position was initially developed by her at Hadassah and initiated by the Israel Cancer Association. Beyond her clinical expertise, at the Hadassah School of Nursing, she lectures and writes in many areas on breast cancer care in general. She served as a board member of the European Oncology Nursing Society, and was also a member of the Editorial Committee of the European Journal of Oncology Nursing. Moreover, she serves as a reviewer for many nursing journals. She was involved in a mutual international collaborative project with nurses in Tianjin, China. She was there for seminars and initiated some cross-cultural research in the area of partners of women with breast cancer, involving both countries. Moreover, she has been invited to Cyprus, Greece Russia, and Turkey to teach and give workshops.
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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 | 0.017 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".