Analysis of the Scientific Production of Nursing in Older People with Cancer in Palliative Care: Bibliometric Study
Bibliographic record
Abstract
Aims: to analyze and map the international production on Nursing Care for the elderly with cancer in palliative care, between 2000 and July 2021.
 Methods: this is a descriptive, exploratory and mixed-approach study that can be classified both a bibliometric and scientometric research. Data collection took place from June to July 2021, in the Scopus (Elsevier) database. After defining the inclusion and exclusion criteria, the collection began, which resulted in the final sample consisting of 198 publications. After the entire selection process, the dataset was saved in a single file in CSV Excel format, where it was later exported to the VOSviewer® software.
 Results: it was shown that most selected publications are concentrated between the years 2019 with 44 (22.22%) publications, 2018 with 37 (18.69%) publications. The United States appears as the country with the largest majority of co-authors, with 81 (40.91%) documents and 750 citations, and the first 15 authors who most published on the subject are from the United States, followed by authors from Canada and Sweden.
 Conclusion: this review showed that publications covering the subject are still limited when related to nursing. However, despite the few existing publications, there is a growing increase in the quantity of publications over the years, which indicates the relevance that the topic has gained in the academic world.
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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 | 0.010 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.094 | 0.148 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".