The Evolution of Worldwide Nurse-Led Cancer Research in the Last 2 Decades (2004–2022)
Bibliographic record
Abstract
BACKGROUND: Research led by nurses has evolved rapidly over the last 2 decades globally. Assessing the work that has been conducted so far can help the specialty to strategically shape future directions of nurse-led cancer research. OBJECTIVE: The aim of this study was to provide a comprehensive, up-to-date synthesis of all nurse-led cancer research published articles over 20 years. METHODS: A bibliometric analysis was used. Three databases were used to retrieve nurse-led cancer research publications for the period from January 1, 2004, to March 11, 2022. RESULTS: A total of 7043 original articles were retrieved. A significant increase in nurse-led cancer research over the past 2 decades was evident. The United States and United Kingdom were the most productive countries in terms of the number of published articles. Minimal international collaboration was observed among low- or middle-income countries versus high-income countries. Breast cancer, palliative care, and quality of life received the most attention in nurse-led cancer research, followed by education, pain, and communication. Very few publications addressed cancer prevention, breaking bad news, and cancer rehabilitation. CONCLUSION: Areas to consider in the future include more international collaborations on commonly agreed research agendas, capacity building to allow more research beyond the few countries that dominate the publications, and more focus on low- or middle-income countries. IMPLICATIONS FOR PRACTICE: The findings of this study provide direction for future research led by cancer nurses and the areas that warrant further investigation.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".