Evidence map›Paper›PMID 38539198›Full record

ArticleBMC nursing2024

Dual mediating effects of anxiety to use and acceptance attitude of artificial intelligence technology on the relationship between nursing students' perception of and intention to use them: a descriptive study.

Kyong Ah Cho, Yon Hee Seo

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Article in BMC nursing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed, 1 pooled it
–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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3 · Its place in the literature

Who cites it

35 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

2 authors.

Kyong Ah ChoDepartment of Nursing, Gwangju University, 277, Hyodeok-ro, Nam-gu, GwangJu, South Korea.ORCID http://orcid.org/0000-0002-2473-3968
Yon Hee SeoDepartment of Nursing, Andong National University, 1375, Gyeongdong-ro, Andong-si, Gyeongsangbuk-do, South Korea. yseo017@naver.com.ORCID http://orcid.org/0000-0002-1344-3607

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI)-based healthcare technologies are changing nurses' roles and enhancing patient care. However, nursing students may not be aware of the benefits, may not be trained to use AI-based technologies in their practice, and could have ethical concerns about using them. This study was conducted to identify the dual mediating effects of anxiety to use and acceptance attitude toward AI on the relationship between perception of and intentions to use AI among nursing students in South Korea.

methodsThe research model followed the PROCESS Macro model 6 proposed by Hayes. The participants were 180 nursing students in Gyeonggi-do. Data were collected from January 5-16, 2023, using self-reported questionnaires. Data were analyzed using the SPSS/WIN 25.0 program, with independent t-tests, one-way analysis of variance, Pearson's correlations, and Hayes's PROCESS macro method for mediation.

resultsAI perception positively correlated with acceptance attitude (r =.44, p <.001), intention to use AI (r =.38, p <.001) and negatively correlated with anxiety (r = -.27, p <.001). Anxiety about AI negatively correlated with an acceptance attitude toward AI (r = -.36, p <.001) and intentions to use AI (r = -.28, p <.001). Acceptance attitude toward AI positively correlated with intentions to use AI (r =.43, p <.001). Anxiety about AI and acceptance attitude toward AI had a dual mediating effect on the relationship between AI perception and intentions to use AI.

conclusionsIt is necessary to develop systematic educational programs to improve the perception of AI. Thus, the competency and professionalism of nursing students regarding the use of AI in healthcare can be improved.

Indexed as

AIArtificial IntelligenceIntention to use AINursing studentPerception

Identifiers

PMID38539198
PMCPMC10976840

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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.