TEXTO & CONTEXTO ENFERMAGEM JOURNAL: A BIBLIOMETRIC STUDY
Bibliographic record
Abstract
ABSTRACT Objective: To analyze scientific production of Texto & Contexto Enfermagem journal available on Scopus. Method: This is a bibliometric study, with a search carried out in November 2024, using the ISSN “0104-0707”. Descriptive statistics were applied using the bibliometrix R software and the Biblioshiny application. Results: A total of 2,038 papers were indexed between 2009 and 2024, with a predominance of original articles (1,882; 92.3%) and 90 (4.4%) literature reviews. Brazil produced the largest number (1,864; 91.5%), followed by Portugal, Canada and the United States of America. The most frequent keywords were “Nursing”, “Nursing Care”, “Primary Health Care”, “Family” and “Patient Safety”. A total of 5,418 authors were identified, with an average of 4.5 per document and international co-authorship of 16.7%. Cite Score increased from 0.5 in 2011 to 1.8 in 2023. The most cited articles were theoretical-methodological, evidencing that methodological rigor is essential to drive scientific and technological progress. Conclusion: The growth and visibility of scientific production of Texto & Contexto Enfermagem journal, which comes from university institutions, at national and international level, was identified. There is a wide range of topics and the authors have formed international collaboration networks.
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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 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.184 | 0.233 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".