Evidence map›Paper›PMID 41202291›Full record

ArticleJournal of medical Internet research2025

Nurses' Perspectives on Evidence Dissemination Barriers and Large Language Model-Based Support: Qualitative Study Using Focus Groups and Nominal Group Technique.

Junyi Ruan, Yimin Tang, Zhongyu Wei, Weijie Xing, Yan Hu

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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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

5 authors.

Junyi RuanSchool of Nursing, Fudan University, Shanghai, China.ORCID https://orcid.org/0009-0002-5542-3820
Yimin TangSchool of Nursing, Fudan University, Shanghai, China.ORCID https://orcid.org/0009-0000-3212-6634
Zhongyu WeiSchool of Data Science, Fudan University, Shanghai, China.ORCID https://orcid.org/0000-0003-3789-8507
Weijie XingSchool of Nursing, Fudan University, Shanghai, China.ORCID https://orcid.org/0000-0002-1327-1059
Yan HuSchool of Nursing, Fudan University, Shanghai, China.ORCID https://orcid.org/0000-0001-6506-0694

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCurrent evidence dissemination methods fall short of meeting clinical nurses' needs, hindering the implementation of evidence-based nursing practice. Large language models (LLMs), with their advanced natural language processing capabilities, offer potential as innovative tools to facilitate evidence dissemination. However, general-purpose LLMs typically lack domain-specific knowledge, are insufficient to support effective evidence dissemination in clinical contexts. It is essential to develop artificial intelligence tools tailored to nurses' needs and preferences to enhance evidence dissemination.

objectiveThe aim of this study is to identify the challenges and barriers clinical nurses face in disseminating evidence, examine their perspectives on the use of existing LLMs to support evidence dissemination, and explore their needs and preferences regarding an LLM-based nursing evidence question-answering system.

methodsThis qualitative study used a combined method of focus group discussions and the nominal group technique (NGT). Using purposive sampling, nurses with diverse specialties, professional titles, and years of experience were recruited, resulting in a total of 22 clinical nurses who completed the entire study. A total of 2 focus group discussions were conducted online via Tencent Meeting between November and December 2024 to explore the challenges and barriers nurses face in disseminating evidence, as well as their perspectives on using existing LLMs to support evidence dissemination. The data were analyzed using qualitative content analysis following the approach of Graneheim and Lundman. Subsequently, the NGT was used between March and April 2025 to identify nurses' needs and preferences for the system to be developed. To overcome geographical constraints and participants' busy schedules, the NGT was conducted entirely online, using online questionnaires and WeChat groups. Overall, 2 rounds of voting were conducted to determine the priority ranking of the functionalities.

resultsThe focus group yielded 3 main themes and 7 subthemes. Three main themes were identified as (1) pathways for evidence dissemination among nurses, (2) barriers that hinder the effective dissemination of evidence, and (3) advantages and limitations of using LLMs to support evidence dissemination. The limitations of current LLMs served as the foundation for nurses' subsequent reflections in the nominal group discussions on the desired functions of a newly developed LLM. The NGT sessions ultimately identified 9 desired functions. After prioritization, the top 3 ranked functions were evidence-based, high-quality question-answering, evidence source provision, and personalized evidence recommendation.

conclusionsThe current evidence dissemination process faces multiple barriers. LLMs hold promise as innovative tools to support evidence dissemination, but require further refinement. Clinical nurses have identified key functional needs, guiding the development of LLMs specifically tailored to clinical nursing practice.

Indexed as

Attitude of Health PersonnelEvidence-Based NursingInformation DisseminationLanguageNursesAdultFemaleFocus GroupsHumansLarge Language ModelsMaleNatural Language ProcessingQualitative Researchevidence-based nursingevidence disseminationfocus grouplarge language modelsnominal group technique

Identifiers

PMID41202291
PMCPMC12639338

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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.