Evidence map›Paper›PMID 42527115›Full record

ArticleJournal of nursing management2026

Cross-Cultural Applicability and Application of the Nursing Leaders' Readiness for Artificial Intelligence Scale: A Cross-Sectional Study.

Hui Yang, Yuanzhi Guo, Yaxin Qiao, Weihui Bai, Cancan Chen, Hengyu Hu

Abstract read
In one paragraph

Article in Journal of nursing management, 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

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

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.

Hui YangNursing Department, Henan Provincial People's Hospital, Henan Provincial Intelligent Nursing and Transformation Engineering Research Center, Zhengzhou University People's Hospital, Henan University People's Hospital, Zhengzhou Henan, 450003, China, hnsrmyy.net.
Yuanzhi GuoDepartment of Medical Oncology, Henan Provincial People's Hospital, Henan Provincial Intelligent Nursing and Transformation Engineering Research Center, Zhengzhou University People's Hospital, Henan University People's Hospital, Zhengzhou Henan, 450003, China, hnsrmyy.net.ORCID https://orcid.org/0009-0005-4553-8597
Yaxin QiaoDepartment of Thoracic Surgery, Henan Provincial People's Hospital, Henan Provincial Intelligent Nursing and Transformation Engineering Research Center, Zhengzhou University People's Hospital, Henan University People's Hospital, Zhengzhou Henan, 450003, China, hnsrmyy.net.
Weihui BaiNursing Department, Henan Provincial People's Hospital, Henan Provincial Intelligent Nursing and Transformation Engineering Research Center, Zhengzhou University People's Hospital, Henan University People's Hospital, Zhengzhou Henan, 450003, China, hnsrmyy.net.
Cancan ChenNursing Department, Henan Provincial People's Hospital, Henan Provincial Intelligent Nursing and Transformation Engineering Research Center, Zhengzhou University People's Hospital, Henan University People's Hospital, Zhengzhou Henan, 450003, China, hnsrmyy.net.ORCID https://orcid.org/0000-0002-7836-8054
Hengyu HuNursing Department, Henan Provincial People's Hospital, Henan Provincial Intelligent Nursing and Transformation Engineering Research Center, Zhengzhou University People's Hospital, Henan University People's Hospital, Zhengzhou Henan, 450003, China, hnsrmyy.net.ORCID https://orcid.org/0009-0005-5892-7380

Funding

Henan Provincial Key Laboratory of Nursing Medicine Fund HNSYHLKT202409Henan Provincial Science and Technology Research Project LHGJ20250051
6 · The paper itself

Abstract

aimsTo translate, revise and evaluate the Chinese-version Nursing Leaders' Readiness for Artificial Intelligence Scale and assess Chinese nursing leaders' AI readiness.

designCross-sectional survey.

methodsThe research team conducted translation, cognitive interviews, a pilot survey and psychometric evaluation. Survey participants were 762 nursing managers. The reliability and validity of the Chinese-version scale were examined. The status and influencing factors of AI readiness among Chinese nursing managers were investigated.

resultsThe translated scale comprises 20 items. Cronbach's α was 0.824. Regarding validity, the three-factor model demonstrated a good fit. The square roots of the average variance extracted, absolute values of correlation coefficients among dimensions and content validity index values were acceptable. The mean AI readiness score was 65.27 ± 9.78. Of the participants, 624 scored above 60 points, 136 scored 40-60 points and 2 scored below 40 points. Scores differed significantly by hospital type, hospital level, department, training participation, prior AI tool or system use and frequency of AI usage.

conclusionThe Chinese-version scale demonstrated good reliability and validity and can effectively assess AI readiness among Chinese nursing managers. Nursing leaders generally demonstrated a favourable level of AI readiness; however, factors such as hospital type and level remain significant determinants influencing nursing leaders' AI readiness. IMPLICATIONS FOR NURSING MANAGEMENT: This study provides a standard measurement tool for Chinese hospitals to check how ready their nursing managers are for AI. These findings help us understand the current state of AI readiness among nursing managers in China. The results can help improve AI use strategies. In addition, they can help bring AI into nursing practice more smoothly. This study has important value for moving forward with digital change and smart nursing in China.

Indexed as

Artificial IntelligenceNurse AdministratorsPsychometricsAdultChinaCross-Cultural ComparisonCross-Sectional StudiesFemaleHumansLeadershipMaleMiddle AgedReproducibility of ResultsSurveys and Questionnairesa cross-sectional studycross-cultural applicabilitynursing leaderreadiness for artificial intelligence

Identifiers

PMID42527115
PMCPMC13420332

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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.