ArticleJournal of clinical nursing2026
Developing and Evaluating the Use of ChatGPT as a Screening Tool for Nurses Conducting Structured Literature Reviews: Proof of Concept Study Results.
Article in Journal of clinical nursing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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.
- Developing and Evaluating the Use of ChatGPT as a Screening Tool for Nurses Conducting Structured Literature Reviews: Proof of Concept Study Results.Journal of clinical nursing · 2026Article
- AI in Health care: A Catalyst for Enhancement, Not Replacement.Journal of clinical nursing · 2026Article
- Outcomes, Evaluation Metrics, and Measurement Tools for Large Language Model Applications Research in Nursing: A Scoping Review.International nursing review · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimTo examine the feasibility of using a large language model (LLM) as a screening tool during structured literature reviews to facilitate evidence-based practice.
designA proof-of-concept study.
methodsThis paper outlines an innovative method of abstract screening using ChatGPT and computer coding for large scale, effective and efficient abstract screening. The authors, new to ChatGPT and computer coding, used online education and ChatGPT to upskill. The method was empirically tested using 400 abstracts relating to public involvement in nursing education from four different databases (CINAHL, Scopus, ERIC and MEDLINE), using four versions of ChatGPT. Results were compared with a human nursing researcher and reported using the CONSORT 2010 extension for pilot and feasibility trials checklist.
resultsChatGPT-3.5 Turbo was most effective for rapid screening and had a broad inclusionary approach with a false-negative rate lower than the human researcher. More recent versions of ChatGPT-4, 4 Turbo, and 4 omni were less effective and had a higher number of false negatives compared to ChatGPT-3.5 Turbo and the human researcher. These more recent versions of ChatGPT did not appear to appreciate the nuance and complexities of concepts that underpin nursing practice.
conclusionLLMs can be useful in reducing the time nurses spend screening research abstracts without compromising on literature review quality, indicating the potential for expedited synthesis of research evidence to bridge the research-practice gap. However, the benefits of using LLMs can only be realised if nurses actively engage with LLMs, explore LLMs' capabilities to address complex nursing issues, and report on their findings. IMPLICATIONS FOR THE PROFESSIONAL AND/OR PATIENT CARE: Nurses need to engage with LLMs to explore their capabilities and suitability for nursing purposes. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.
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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.