Evidence map›Paper›PMID 36781691›Full record

SynthesisMaternal and child health journal2023

Fatigue During Pregnancy: A Bibliometric Analysis.

Chin-Lan Yang, Ching-Yi Chang

Abstract readSystematic Review
PubMed Publisher
In one paragraph

Synthesis in Maternal and child health journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Trial
  2. Article
  3. Review
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

2 authors.

Chin-Lan YangDepartment of the Nursing, Hsin Sheng Junior College of Medical Care and Management, Department of the Nursing, National Taipei University of Nursing and Health Sciences, No. 365, Ming-Te Rd., Peitou District, 11219, Taipei, Taiwan.
Ching-Yi ChangSchool of Nursing, College Of Nursing, Taipei Medical University, 250 Wuxing Street, 11031, Taipei, Taiwan (ROC). frinng.cyc@gmail.com.ORCID http://orcid.org/0000-0002-3146-2270

Funding

Ministry of Science and Technology, Taiwan MOST 111-2410-H-038-029-MY2
6 · The paper itself

Abstract

objectiveThis study uses a systematic review with bibliometric analysis methods to investigate the characteristics of the most cited research papers in the field of nursing fatigue and pregnancy.

methodsIn the Web of Science (WoS) database. We used the keywords "fatigue" and "pregnancy" to search for articles published from 2000 to 2020, limited to SSCI and Science Citation Index (SCI) journal-type articles. This study identifies the most cited studies in the WoS database based on PRISMA guidelines (Preferred reporting items for systematic reviews and meta-analyses). These studies form the research data, then visualized and analyzed using a retrospective bibliometric analysis and VOSviewer.

resultsThe studies in the dataset were analyzed in 319 different journals in 51 countries between 2000 and 2020. The study was found that the United States (US) was the country with the highest yield. The most frequent keywords were postpartum, depression, sleep, and postpartum depression.

conclusionThe research results further laid the foundation of bibliometrics for scholars and identified researchers, scientific journals, countries, and hot topics for fatigue-related pregnancy literature. Journals with high impact factors contain the most cited research and open new horizons for research in the nursing field of pregnancy-related fatigue, thus providing research inspiration for investigators in this field.

Indexed as

BibliometricsDatabases, FactualFemaleHumansRetrospective StudiesUnited StatesDepressionFatiguePostpartum depressionPregnancy symptoms

Identifiers

What OpenQuestion holds

Textmetadata
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.