Oncology nursing in the Eastern Mediterranean Region: listening to the workforce
Bibliographic record
Abstract
BACKGROUND: Over half the countries in the World Health Organization (WHO) Eastern Mediterranean Region (EMR) are experiencing conflict or are socially fragile, compromising cancer care. Nonetheless, throughout the EMR, competent nurses are major players in the cancer care team. The aim of this paper is to portray the challenges and opportunities for oncology nursing in the EMR. METHODS: This paper draws upon the relevant literature on oncology nursing across EMR with a focus on Afghanistan, Lebanon, Somaliland, and Iran. To enhance the scant nursing literature and obtain real-life experiences, short interviews were undertaken with nine nurses and two doctors, personal contacts of the authors, working in cancer care in those countries. RESULTS: Against the general background of vast economic constraints in health services, the lack of recognition of oncology nursing as a speciality and high rates of nurse migration, many oncology nurses in EMR are fighting for professional recognition and some are working under unsafe conditions. Undeterred by these circumstances, nurses are making every effort to care compassionately for people with cancer. CONCLUSIONS: The perspectives of the cancer workforce in EMR both foster an appreciation of cultural diversity and provide the evidence and motivation for oncology nurses worldwide to further collaborate via global nursing organisations to strive for country-specific recognition and change in nursing practice.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".