How does nursing research differ internationally? A bibliometric analysis of six countries
Bibliographic record
Abstract
BACKGROUND: International nursing research comparisons can give a new perspective on a nation's output by identifying strengths and weaknesses. AIM: This article compares strengths in nursing research between six mainly English-speaking nations (Australia, Canada, Ireland, New Zealand, United Kingdom and United States). METHODS: Journal authorship (percentage of first authorship by nationality) and article keywords were compared for Scopus-indexed journal articles 2008-2018. Three natural language processing strategies were assessed for identifying statistically significant international differences in the use of keywords or phrases. RESULTS: Journal author nationality was not a good indicator of international differences in research specialisms, but keyword and phrase differences were more promising especially if both are used. For this, the part of speech tagging and lemmatisation text processing strategies were helpful but not named entity recognition. The results highlight aspects of nursing research that were absent in some countries, such as papers about nursing administration and management. CONCLUSION: Researchers outside the United States should consider the importance of researching specific patient groups, diseases, treatments, skills, research methods and social perspectives for unresearched gaps with national relevance. From a methods perspective, keyword and phrase differences are useful to reveal international differences in nursing research topics.
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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 | 0.029 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.153 | 0.250 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".