Evidence map›Paper›PMID 42001146›Full record

ArticleBMC health services research2026

Heterogeneity in nurses' attitudes toward artificial intelligence: a latent profile analysis.

Xu Li, Huiting Xu, Xu Hu, Jingjing Guo, Pin Yu, Hailing Ju

Abstract read
In one paragraph

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

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0citing papers in PubMed
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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

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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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

6 authors.

Xu Li *Department of Central ICU, The First Affiliated Hospital of Soochow University, Suzhou, China.
Huiting Xu *School of Medicine, Tongji University, Shanghai, China.
Xu Hu *Department of Central ICU, The First Affiliated Hospital of Soochow University, Suzhou, China.
Jingjing GuoDepartment of Central ICU, The First Affiliated Hospital of Soochow University, Suzhou, China.
Pin YuDepartment of Central ICU, The First Affiliated Hospital of Soochow University, Suzhou, China. ayou8011@126.com.
Hailing JuDepartment of Nursing, Shanghai Tenth People's Hospital, Shanghai, China. jhling_dw@163.com.

Funding

the "Zhou's" nursing research project of The First Affiliated Hospital of Soochow University Program No. HLYJ-Z-202502
6 · The paper itself

Abstract

backgroundThe rapid development of artificial intelligence (AI) is reshaping healthcare delivery and nursing practice. However, nurses’ acceptance of AI varies considerably. Identifying distinct patterns of AI acceptance and their associated characteristics may help inform tailored strategies for the integration of AI technologies into nursing practice.

methodsBetween November and December 2025, a total of 595 nurses were recruited using a convenience sampling method. Data were collected using a general demographic questionnaire and the Artificial Intelligence Attitude at Work (AAAW) scale. Latent profile analysis was conducted to identify distinct subgroups of nurses based on their AI attitude patterns. Univariate analyses and multinomial logistic regression were used to explore factors associated with profile membership.

resultsThree latent profiles of AI attitudes were identified: Low AI Acceptance–Cautious Observational Profile (25.7%), Moderate AI Acceptance–Rational Evaluative Profile (31.8%), and High AI Acceptance–Utility-Oriented Profile (42.5%). Multinomial logistic regression analysis revealed that sex, age, educational level, professional title, nursing hierarchy level, and hospital level were significantly associated with profile membership. Female nurses, those with higher educational attainment, intermediate professional titles, lower nursing hierarchy levels, and those working in tertiary hospitals were more likely to belong to the high AI acceptance profile, whereas nurses aged ≥ 40 years were less likely to be classified into this profile.

conclusionsNurses’ attitudes toward artificial intelligence are heterogeneous and can be categorized into distinct latent profiles.Individual characteristics, professional roles, and organizational context were associated with AI acceptance profiles among nurses.

trial registrationNot applicable.

Indexed as

Artificial IntelligenceAttitude of Health PersonnelNursesNursing Staff, HospitalAdultFemaleHumansMaleMiddle AgedSurveys and QuestionnairesArtificial intelligenceAttitudeInfluencing factorsLatent profile analysisNurses

Identifiers

PMID42001146
PMCPMC13156861

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.