Cancer nursing research output and topics in the first decade of the 21st century: results of a bibliometric and co-word cluster analysis.
Bibliographic record
Abstract
OBJECTIVE: Many countries carry a high cancer burden and comprehensive cancer nursing is becoming increasingly complicated and difficult. Summarizing the recent research focus on cancer nursing may provide a snapshot of this field for those nurses or nurse educators who are in need of a quick overview of the research and its utilization. METHODS: Candidate publications from January 1st 2001 to March 31st 2011 were collected by searching PubMed with the MeSH word 'oncologic nursing' and without language restriction. Bibliometric techniques used in this study included a statistical analysis of publication counts by authors, countries and journals and a co-word cluster analysis of highly-frequent MeSH words. RESULTS: A total of 2933 publications about cancer nursing from 246 journals were indexed in PubMed, with Oncology Nursing Forum identified as the top contributing journal in the field. The United States, the United Kingdom and Canada were the largest three producer countries about cancer nursing. A total of 34 highly-frequent MeSH words for more than 100 times' occurrences in the papers about oncologic nursing were extracted for cluster analysis. These words were classified into 3 aspects: (1) nursing practice; (2) nursing evaluation and education; (3) nursing-related social support. CONCLUSIONS: Stable growth has occurred in the research field of cancer nursing. The limited amount of the publications from developing countries indicates that the field is still under-developed. Emerging topics of nurse-patient relations and social support provide some hints of the need to provide more target training for the nurses and nurse students in the field of cancer nursing.
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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 | 0.019 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.148 | 0.225 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".