Back

Global Trends in Inflammatory Bowel Disease Nursing Research (2000-2024)

Zhou, X.; Zeng, X.; Ma, Y.; Zhong, Q.; Luo, Q.; Li, H.; Luo, H.-l.

2025-10-14 gastroenterology
10.1101/2025.10.12.25337837 medRxiv
Show abstract

In this study, we examined the current landscape of global research on nursing care for inflammatory bowel disease (IBD), identified key research hotspots and emerging trends, and provided a reference for guiding future investigations. A comprehensive literature search was conducted in the Web of Science core database covering the period from January 1, 2000, to December 31, 2024. CiteSpace software was used to perform a bibliometric and visual analysis. Publication trends, country and institutional contributions, author networks, keyword co-occurrence and clustering, and citation patterns were analyzed. In total, 2,573 articles were included. The volume of publications in the field of nursing for IBD steadily increased over the past two decades. The USA produced the highest number of publications, with Brigham and Womens Hospital--affiliated with Harvard University--leading in terms of publication output. Andrew Chan was identified as the most prolific author. The Lancet had the highest citation frequency. Cluster analysis revealed 15 distinct clusters, with the core research population focusing on pediatric IBD care. The cluster term "quality of life" emerged as a high-frequency keyword that remained active from 2010 to 2024 and was closely linked to chronic disease management. "Coronary heart disease" showed the strongest citation burst, highlighting a growing interest in IBD-related comorbidities. In summary, global IBD research is centered around major academic institutions such as Harvard University, with a primary focus on disease management. Emerging trends in the field include an increasing emphasis on psychosocial support, complication prevention, and comorbidities, with a progressive shift toward precision nursing.

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.