ArticleBMC nursing2025
Editorial stances on large Language models in leading nursing publications: a cross-sectional 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 3 papers, 1 of them a synthesis that pooled 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.
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
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Registered nurses' experiences with generative artificial intelligence: a meta-synthesis of qualitative studies.Frontiers in public health · 2026Pooled it
- Article
- Mind the gap: A cross-sectional analysis of large language model guidance in emergency medicine journals.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe rapid integration of large language models (LLMs) into scholarly publishing has created an urgent need for clear standards. This study aims to comprehensively analyze the editorial stances of leading nursing publications regarding the use of LLMs in manuscript preparation and peer assessment.
methodsWe conducted a cross-sectional analysis of the top 50 nursing publications according to their journal impact factor. Each publication's website was systematically evaluated for directives concerning LLM use in authorship, content generation, image creation, and peer assessment. Journal metrics were also extracted to assess any correlation with policy adoption.
resultsOf the 50 publications, 35 (70%) had explicit LLM-related directives. A strong point of agreement permits the use of LLMs for content generation (97%) but prohibits LLM authorship (94%). However, a significant divergence was found regarding AI-generated images, with 52% of publications prohibiting their use. Guidance on LLM use in peer assessment was also inconsistent, with 49% of publications prohibiting it. Policy adoption varied significantly by publisher (ranging from 20% to 100%). No statistical association was found between policy existence and journal impact metrics (p > 0.05).
conclusionsLeading nursing publications exhibit a fractured landscape on LLM use. While foundational agreement exists on authorship and content generation, critical areas like image creation and peer assessment lack consistent standards. This ambiguity underscores the need for a more unified, transparent framework to guide ethical and responsible LLM integration in nursing scholarship.
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What OpenQuestion holds
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.