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Record W3093563902 · doi:10.1111/jan.14564

The state of nursing research from 2000 to 2019: A global analysis

2020· article· en· W3093563902 on OpenAlexaboutno aff
Yanbing Su, Liu Hua, Chao Liu, Fenglan Wang, Zhiguang Duan

Bibliographic record

VenueJournal of Advanced Nursing · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsNursing researchCitation impactBibliometricsPolitical scienceCitationNursingMedicineLibrary science

Abstract

fetched live from OpenAlex

AIM: This study aims to present a general bibliometric overview of the development status of global nursing research from 2000 to 2019. DESIGN: A longitudinal bibliometric analysis of nursing research was conducted. METHODS: Nursing research publications (N = 88,665) were obtained from Web of Science. Bibliometric method was used to map the output and citation impact trends of countries/regions, institutions, disciplines, and journals and analyse the research collaboration among countries/regions and institutions. RESULTS: The global paper output in nursing research increased steadily over the past two decades and it varied in different countries/regions with the USA being far ahead of the others. The paper output and cross-border collaboration are mainly distributed in several developed countries like the USA, the UK, Australia, and Canada. The University of Pennsylvania, Harvard University, University of Toronto, and University of North Carolina at Chapel Hill have high academic influence in the field of nursing. Increasing attention from academic fields has been paid to research on nursing. Journal of Advanced Nursing is the most prolific and most cited journal in nursing field. CONCLUSION: Nursing research has developed steadily over the last two decades. Both the scientific output and research collaboration are disproportionally distributed between high-income countries/regions and low- and middle-income countries/regions. Most research and collaboration have taken place in a few developed countries across North America, Europe, and Oceania. IMPACT: The study highlighted the need for policy makers and funding agencies, especially those from low- and middle-income countries/regions, to allocate research funding that supports the nursing higher education and international cooperation so as to promote the development of high-quality nursing research in those countries/regions. At the same time, researchers from non-English-speaking countries/regions should attach more importance to publishing papers in English, strengthening the academic exchanges with international nursing colleagues and better integrating into the international academic community.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.811
Threshold uncertainty score0.764

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.213
GPT teacher head0.610
Teacher spread0.397 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations59
Published2020
Admission routes1
Has abstractyes

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