ArticleBMC nursing2025
Patterns and trends in infection control nursing research: text network analysis.
Article in BMC nursing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Unmet Healthcare Needs in COPD: A Text Network Analysis and Topic Modeling of Pre/Post-COVID-19 Research Trends.Healthcare (Basel, Switzerland) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDespite the advancement of the medical environment, health-related infections continue to increase. The study was to explore the knowledge structure of infection control nursing research using keywords and networks. It was also to identify theoretical groundings regarding the current infection control nursing theory and presented the area and level of future theory.
methodsA quantitative content analysis design was employed. Using text network analysis, keywords were derived from the abstracts of 2,651 studies conducted from 1974 to 2022, and centrality and network connection structure analyses were used to identify the keywords' structure and characteristics.
resultsIn the frequency of occurrence, vaccine is the most frequent, followed by health personnel, influenza, and glove. The connection structure analyzed using weights that the terms influenza and vaccine appeared simultaneously, followed by injury and needle, hepatitis and vaccine, immunization and vaccine, and injury and needlestick injury in order. The words health personnel, vaccine, and glove, which all showed high centrality, indicate that they are keywords studied in relation to various words in infection control nursing research.
conclusionThis study confirmed that keywords revealed through text network analysis were medical workers, vaccines, and gloves. Vaccine was closely related to terms such as influenza, hepatitis, and healthcare provider, while the term gloves was closely related to respiratory protection, injury and needles, and healthcare provider. Infection control for healthcare providers is based on evidence-based knowledge and practices, and quality evaluation of the evidence and verification of effectiveness are required. CLINICAL TRIAL NUMBER: Not applicable.
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Registered trials
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.