Evidence map›Paper›PMID 42374350›Full record

ArticleBMC medical education2026

Innovation capability characteristics among nursing undergraduates: a latent profile analysis.

Xi Wang, Zihan Liu, Rong Zhang, Jinxiu Liu, Hong Ye, Song Wang

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Article in BMC medical education, 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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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Xi WangSchool of Nursing, Bengbu Medical University, No. 2600 Donghai Avenue, Bengbu, Anhui Province, China. wangxiwx12390@sina.com.
Zihan LiuSchool of Nursing, Bengbu Medical University, No. 2600 Donghai Avenue, Bengbu, Anhui Province, China.
Rong ZhangSchool of Nursing, Bengbu Medical University, No. 2600 Donghai Avenue, Bengbu, Anhui Province, China.
Jinxiu LiuSchool of Nursing, Bengbu Medical University, No. 2600 Donghai Avenue, Bengbu, Anhui Province, China.
Hong YeSchool of Nursing, Bengbu Medical University, No. 2600 Donghai Avenue, Bengbu, Anhui Province, China.
Song WangSchool of Nursing, Bengbu Medical University, No. 2600 Donghai Avenue, Bengbu, Anhui Province, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInnovation capability is a core competency for nursing college students to adapt to the development of modern nursing and meet clinical demands. This study applied innovation ecosystem theory and aimed to identify the latent profile types of nursing college students' innovation capability and their associated factors, to provide a basis for formulating targeted training strategies.

methodsA cross-sectional survey was conducted among 1,086 nursing college students using a self-designed "Questionnaire on Innovation Capability of Nursing College Students" with good reliability and validity. Latent profile analysis (LPA) was used to classify the innovation capability types.

resultsThe innovation capability of nursing college students was divided into four latent profile types: Low Innovation Capability Type (22.65%), Low-Medium Innovation Capability Type (32.97%), Medium-High Innovation Capability Type (29.93%) and High Innovation Capability Type (14.46%), showing a distribution characteristic of "larger in the middle and smaller at both ends". Multiple linear regression analysis showed that weekly self-directed learning time for innovation knowledge (β'=0.298), number of innovation competition participations (β'=0.234), clinical internship experience (β'=0.189) and age (analyzed as a continuous variable, β'=0.123) were significant positive predictors of innovation capability (all P < 0.001). The total score of innovation capability of nursing college students was 67.64 ± 8.36, with the lowest average score in the Innovation Environment dimension (2.58 ± 0.52) and the highest in the Innovation Motivation dimension (2.92 ± 0.50).

conclusionNursing college students' innovation capability presents obvious heterogeneous characteristics with four distinct profile types. Weekly self-directed learning time for innovation knowledge, participation in innovation competitions, clinical internship experience and age were identified as key factors associated with higher innovation capability. These findings suggest that targeted training strategies such as differentiated cultivation, strengthening clinical innovation training, improving self-directed learning ability and optimizing innovation incentive mechanisms may help enhance nursing college students' innovation capability. Longitudinal and interventional studies are warranted to confirm these effects.

Indexed as

Education, Nursing, BaccalaureateStudents, NursingAdultCross-Sectional StudiesFemaleHumansMaleSurveys and QuestionnairesYoung AdultCapabilityEducationInnovationNursingUndergraduates

Identifiers

PMID42374350
PMCPMC13595712

What OpenQuestion holds

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Registered trials

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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.