Evidence map›Paper›PMID 42459481›Full record

ArticleFrontiers in public health2026

Artificial intelligence readiness and its relationship with thriving at work among Chinese nurses: a latent profile analysis.

Xinyue Chen, Wanyu Ding, Xueyan Wang, Yingying Wang, Shaoyong Ma, Mingfen Tao

Abstract read
In one paragraph

Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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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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

1 citing paper in PubMed.

  1. Article
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

6 authors.

Xinyue Chen *Graduate School, Wannan Medical University, Wuhu, Anhui, China.
Wanyu Ding *Graduate School, Wannan Medical University, Wuhu, Anhui, China.
Xueyan Wang *Graduate School, Wannan Medical University, Wuhu, Anhui, China.
Yingying Wang *Emergency Intensive Care Unit, The First Affiliated Hospital of Wannan Medical University, Wuhu, Anhui, China.
Shaoyong Ma *School of Nursing, Wannan Medical University, Wuhu, Anhui, China.
Mingfen TaoDepartment of Nursing, The First Affiliated Hospital of Wannan Medical University, Wuhu, Anhui, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: To explore the latent profile categories of nurses' artificial intelligence readiness and analyze the relationship between these categories and thriving at work, so as to provide references for nursing managers to develop targeted management strategies to enhance nurses' level of thriving at work. Methods: A cross-sectional survey was conducted from February to April 2026 among 498 nurses from five hospitals in Anhui Province, China. Data were collected using a general information questionnaire, the Medical Artificial Intelligence Readiness Scale, and the Thriving at Work Scale. Latent profile analysis was performed using the 22 artificial intelligence readiness items as manifest indicators. Model selection was based on information criteria, entropy, likelihood-ratio tests, profile size, posterior classification probabilities, parsimony, and interpretability. Chi-square tests, one-way analysis of variance, and multinomial logistic regression were used for exploratory profile comparisons. Results: Three profiles were identified: low artificial intelligence readiness ( Conclusion: Artificial intelligence readiness among nurses is heterogeneous, and approximately one-third of nurses were classified into a high-readiness profile. The level of thriving at work varies among nurses in different latent categories of artificial intelligence readiness. Nursing managers and public health administrators should consider profile-specific strategies that combine targeted artificial intelligence training, clinical practice support, and career development for nurses.

Indexed as

Artificial IntelligenceAttitude of Health PersonnelNursesNursing Staff, HospitalAdultChinaCross-Sectional StudiesEast Asian PeopleFemaleHumansMaleSurveys and Questionnairesartificial intelligence readinesslatent profile analysisnursesnursing managementpublic healththriving at work

Identifiers

PMID42459481
PMCPMC13369271

What OpenQuestion holds

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LicenceCC BY
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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.