Evidence map›Paper›PMID 42410585›Full record

ArticleBMC nursing2026

Latent profiles of nurses' attitudes toward artificial intelligence in nursing and associated factors: a cross-sectional study.

Tingting Wei, Ting Wang, Jiaojiao Ruan, Xueying Pang, Peng Zhou, Zhengyiqing Wang, Mingxia Hu, Shanshan Chen, Xiumei Zhang, Chengcong Li

Abstract read
In one paragraph

Article in BMC nursing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Tingting Wei *Department of General Surgery, Department of Gastrointestinal and Weight Loss Metabolic Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Ting Wang *Department of General Surgery, Department of Gastrointestinal and Weight Loss Metabolic Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Jiaojiao RuanDepartment of General Surgery, Department of Neurosurgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Xueying Pang *Department of General Surgery, Department of Gastrointestinal and Weight Loss Metabolic Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Peng ZhouDepartment of Critical Care Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Zhengyiqing WangDepartment of General Surgery, Department of Neurosurgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Mingxia HuDepartment of General Surgery, Department of Neurosurgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Shanshan ChenDepartment of General Surgery, Department of Neurosurgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Xiumei ZhangDepartment of General Surgery, Department of Gastrointestinal and Weight Loss Metabolic Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China. Zhangxiumei@ahmu.edu.cn.
Chengcong LiDepartment of General Surgery, Department of Neurosurgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China. yfy665341@fy.ahmu.edu.cn.

Funding

2025 Medical Social Work Research Project 20250312the Anhui Province Higher Education Scientific Research Project 2022AH051128the Chinese Nursing Society Scientific Research Project ZHKY202211the Nursing Project of Anhui Institute of Translational Medicine 2024zhyx-hl-B25the Research Project Topics of the Chinese Nursing Association ZHKYQ202402
6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) is being gradually integrated into clinical nursing practice, where it plays an important role in improving nursing quality, reducing nurses' workload, and promoting the intelligent transformation of nursing. Nurses' attitudes toward AI applications in nursing directly affect the promotion and implementation of this technology. Understanding these attitudes and their heterogeneity is crucial for the successful implementation of AI technology. This study aimed to identify potential types of nurses' attitudes toward the use of AI in nursing and to explore the factors associated with profile membership with type affiliation.

methodsA cross-sectional survey was conducted among 206 clinical nurses in Anhui Province, China, in July 2025. Data were collected using a general information questionnaire, the Attitudes Toward the Application of AI Technology in Nursing Scale, and the Multidimensional Nursing Generations Questionnaire. Latent profile analysis(LPA) was used to identify distinct attitude profiles. Univariate analyses and multinomial logistic regression were performed to explore associated factors.

resultsThree profiles were identified: positive acceptance (16.02%), ambivalent balance (9.71%), and cautious skepticism (74.27%). Multinomial logistic regression showed that educational level, computer proficiency, English proficiency, and generational characteristics were significantly associated with profile membership (all P < 0.05).

conclusionNurses showed moderate attitudes toward AI in nursing with substantial heterogeneity. Profile-tailored strategies may help nursing managers facilitate the effective and sustainable implementation of AI technologies in clinical practice.

Indexed as

Artificial intelligenceAttitudesGenerational characteristicsLatent profile analysisNurses

Identifiers

PMID42410585
PMCPMC13411310

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.