Trends in high‐impact papers in nursing research published from 2008 to 2018: A web of science‐based bibliometric analysis
Bibliographic record
Abstract
OBJECTIVE: To assess the overall trends in the development and citation impact of high-impact papers in nursing research worldwide to gain insight into the focus areas of nursing research. BACKGROUND: Bibliometric method is proved to be effective in analysing the papers' characteristics, and it gained considerable interest from the scientific community in recent years. An analysis of the characteristics and intrinsic patterns of high-impact papers in nursing research will provide an objective reflection of the research hot spots. Nursing managers can pointedly increase funding amount and strengthen research cooperation in order to put the scientific results into management practice. METHODS: Bibliometric methods and visualization software were used to comprehensively analyse high-impact papers in nursing research in terms of development trends, countries/regions, distribution of subject areas, research institutes, collaborative networks and subject terms. RESULTS: There were 6,886 papers between 2008 and 2018. The number of papers increased from 528 in 2008 to 723 in 2015, and then remained above 600 in 2016 and 2017. These papers were mainly distributed in nursing, oncology, paediatrics, gynaecology, teaching and education, and cardiac and cardiovascular systems and were cited by 128,845 papers that came from 89 Web of Science subject areas. Papers in nursing research accounted for the largest share of these citations. The top five countries in the world in terms of the number of high-impact papers were the United States, Australia, the United Kingdom, Canada and Sweden. The research institutions with the highest number of high-impact papers worldwide were the University of California System, the University of Pennsylvania, the University of North Carolina, the University of London and the University of Technology Sydney. In this data set, it was shown that research collaborative circles have been formed in the United States, Australia, Canada and Europe; the subject-term analysis indicated that 'women' and 'students' have always been high-interest populations for high-impact papers and that cancer is still one of the greatest threats to human health. Furthermore, the subject terms of high-impact papers in nursing research have gradually evolved from 'disease' and 'therapy' to 'symptoms'. CONCLUSION: In recent years, the number of high-impact papers published each year in nursing research has grown over time. Nursing has been shown to be a highly specialized subject, and the majority of its high-impact papers have been published by research institutions. Although cross-regional collaborations are beginning to emerge, there is much room for improvement in this regard. Finally, women, students, cancer and symptomatic care are the current focus areas in nursing research. IMPLICATIONS FOR NURSING MANAGEMENT: This study informs nursing managers within the nursing research field about subject areas, collaborative networks and hot topics. It is beneficial to pay attention to studies, manage scientific outputs, allocate resources, seek cooperation and improve the work efficiency of scientific research management.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.126 | 0.158 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".