Thirty-Five Years of Knowledge in Transcultural Nursing: A Bibliometric Analysis of <i>Journal of Transcultural Nursing</i>
Bibliographic record
Abstract
Introduction: This bibliometric analysis aims to examine articles published in Journal of Transcultural Nursing ( JTCN ), between 1989 and 2024. Method: This study analyzed 1,675 JTCN publications from 1989 to 2024 using Scopus as a data source. Performance analysis and scientific mapping techniques were used for bibliometric metadology. Analyses were performed with VOSviewer and Bibliometrix software. Results: In total, 1,675 articles were included in the study. The most prolific authors are Leininger, M., Boyle, J.S., Pacquiao, D.F., and Zoucha, R. The leading countries in terms of number of publications are the United States, Canada, and Australia. According to the co-occurrence analysis, six research themes emerged. In addition, it was determined that “nursing care,” “COVID-19,” “social determinants of health,” “health equity,” and “refugees” were trending topics. Discussion: Our findings can provide nurses and academicians with ideas on the subject by identifying trending topics and leading researchers in JTCN .
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.022 | 0.079 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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; both teacher heads agree on what is shown here.
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".