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Integrative review of international nursing research in Mainland China

2009· article· en· W2019012597 on OpenAlexaboutno aff
M. Li, Liu Wei, H. Liu, Lei Tang

Bibliographic record

VenueInternational Nursing Review · 2009
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsnot available
Fundersnot available
KeywordsMainland ChinaChinaBeijingNursing researchNursingGovernment (linguistics)MainlandMedicinePolitical scienceGeography

Abstract

fetched live from OpenAlex

BACKGROUND: Nursing research in Mainland China is divided into two parts: domestic nursing research, comprising all publications in Chinese; and international nursing research, comprising all publications in English. Domestic nursing research has been developing rapidly, demonstrated by the increase in new national or regional journals and publications. However, little is known about the extent of international research. AIMS: To outline the development of international nursing research publications in Mainland China and to provide suggestions for future development. METHOD: All of the papers were retrieved from PubMed. The key search phase was 'China or P.R.China NOT Hong Kong NOT Taiwan NOT Macao [AD]', with the limits of 'English', 'nursing journals' as well as the published date up to '2007/09/30'. FINDINGS: PubMed recorded 57 English papers that were originally conducted in Mainland China during the search period from 1989 to 30 September 2007. Thirty-seven of the total 57 (65%) publications were contributed by Beijing, Shanghai and Hubei. Forty-four of the 57 publications were conducted with collaborators from Hong Kong, the USA, the UK and Canada. Thirteen publications were funded by international societies, while only three were funded by the Chinese government. The research topics mainly focused on clinical research, nursing education and nursing management. CONCLUSIONS AND IMPLICATIONS: This study indicates that international nursing research has been growing slowly in Mainland China along with provincial variations. The suggestions to improve nursing research include the reform of nursing education, the enhancement of the collaboration with the international societies and the establishment of research priorities.

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)0.007
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.831
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.233
GPT teacher head0.646
Teacher spread0.413 · 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.

Study designSystematic review
Domainnot available
GenreReview

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

Citations21
Published2009
Admission routes1
Has abstractyes

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