Knowledge Graph Analysis for Chronic Diseases Nursing based on Visualization Technology and Literature Big Data
Bibliographic record
Abstract
The use of knowledge graph analysis for chronic disease nursing based on visualization technology and literature big data is an unexplored area of research in this field of study. To uncover research hotspots and developmental trends in the field of chronic disease nursing, and to provide a scholarly reference, we employed mathematical and statistical methods along with CiteSpace literature visualization analysis software for quantitative analysis of extensive literature data from the Web of Science Core Collection. We examined aspects such as publication trends, journals, author collaborations, research institutions, national and regional distributions, keyword co-occurrence, clustering, time zones, emergence, literature co-citations, and more. These analyses identified the current hotspots and future directions for research. Notably, scholars' interest in chronic disease nursing exhibited a consistent upward trajectory. In particular, the field of artificial intelligence technology application in nursing yielded $3,610$ published papers in $141$ journals with more than or equal to $10$ published papers on the topic, accounting for $58.41 \%$ of the total number of published papers in this field of study. Furthermore, the top three publishers were the “Journal of Clinical Nursing,” “Journal of Advanced Nursing,” and “BMC Health Services Research.” Among authors, Hu, Frank B., Willett, Walter C., and Rimm, Eric B., ranked as the top three, and 12 authors had more than 10 publications. The most active research institutions included Harvard University, Harvard Medical School, Brigham & Women’s Hospital, University of California System, University of London, US Department of Veterans Affairs, Veterans Health Administration (VHA), Harvard T. H. Chan School of Public Health, University of Sydney, and the University of Toronto. The United States, Australia, England, China, Canada, Netherlands, Spain, Italy, Sweden, and Germany emerged as the leading countries in terms of research output, while emerging hotspots encompassed topics such as incidence, rheumatoid arthritis, qualitative research, burnout, kidney transplantation, critical illness, COVID-19, Sars-COV-2, public health, and the well-being of medical staff. These findings present valuable insights for prospective research endeavors.
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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.048 | 0.032 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".