Evidence map›Paper›PMID 39682076›Full record

ArticleHealth information and libraries journal2025

Government document citations in top nursing journals: A bibliometric analysis.

Andrew Dudash, Stephen Woods, Kathleen Phillips

Abstract read
In one paragraph

Article in Health information and libraries journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Andrew DudashPenn State University Libraries, Paterno Library, University Park, Pennsylvania, USA.ORCID 0000-0002-9546-3440
Stephen WoodsPenn State University Libraries, Paterno Library, University Park, Pennsylvania, USA.
Kathleen PhillipsPenn State University Libraries, Paterno Library, University Park, Pennsylvania, USA.ORCID 0000-0002-0098-6354

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionCitation analysis studies the use of citations found in various types of publications, including the use of government information, and can provide insight into research utilisation in nursing literature.

methodUsing citation analysis, this study examines the use, prevalence and jurisdiction of government information use among seven nursing research journals based on ranking criteria. Government information citations were identified across three types of articles: editorials, reviews and research articles.

resultsNearly two-thirds of the articles in this study cite some form of government information, and the average percentage of government citations is 5.38 of all citations. Government information was identified from national governments, inter-governmental organizations and state and regional governments. DISCUSSION: Understanding the use, prevalence and jurisdiction of government information informs health information professionals allowing them to engage with nursing students and researchers about this important type of information and the types of articles that use government information.

conclusionNursing researchers frequently cite government information in their work. This has implications for librarians, nursing researchers and health professionals. Understanding the intersection of government information and nursing research should inform future practice in evidence-based nursing research and nursing education.

Indexed as

BibliometricsGovernmentPeriodicals as TopicHumansNursing Researchbibliometricsgrey literaturenursing literaturescholarly communication

Identifiers

PMID39682076
PMCPMC12590313

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.