Oncology nursing research: a global perspective
Bibliographic record
Abstract
The specialty of oncology nursing began around the 1970s when oncology as a science became a significant practice in the clinical areas. As the practice of oncology grew in health care settings, physicians focused on providing care for individuals diagnosed with cancer with treatments like surgery, radiation, and novel chemotherapy agents. Physicians treated the physical disease, while oncology nurses cared for, and became specialised in, the assessment and management of side effects and symptoms, and supporting patients and families in coping with the impacts from the disease and treatments. Thus, the oncology nursing speciality focus is on physical, psychosocial, and practical management of patients' care; education of patients and families; and co-ordination of the complex care provided. This article focuses on how the science, or the evidence base, of oncology nursing has grown globally since those early years. The aim of this paper is to illustrate the growth and development in the body of evidence underpinning the oncology nursing specialty by highlighting scientific studies, publications, and evidence-based practice. Over the years, there has been a steady growth in the research evidence supporting the specialty, yet future challenges are ahead. These challenges include demonstrating impact of nursing interventions; infrastructure support; resources for capacity building; building research-mindedness; and strengthening equality, diversity, and inclusion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.033 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.010 | 0.010 |
| 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".