The Relationship Between Anxiety and Readiness Levels Regarding Artificial Intelligence in Midwives: An Intergenerational Comparative Study.

IF 1.9 4区 医学 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Cin-Computers Informatics Nursing Pub Date : 2025-05-01 DOI:10.1097/CIN.0000000000001269
Ayşe Nur Yilmaz, Sümeyye Altiparmak, Remziye Sökmen
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Abstract

This study aimed to compare Generations X, Y, and Z in terms of anxiety and readiness levels regarding artificial intelligence and investigate the relationship between anxiety and readiness levels regarding artificial intelligence in midwives across generations. This study is cross-sectional and comparative with a study sample of 218 midwives working in a province in the east of Turkey. Data were collected with the "Personal Information Form," "Artificial Intelligence Anxiety Scale," and "Medical Artificial Intelligence Readiness Scale." The evaluation of the data was carried out using the IBM SPSS Statistics version 22.0 (IBM Inc., Armonk, NY, USA) package program. Descriptive statistics, a one-way analysis of variance test, Pearson correlation, and regression analysis were used to analyze the data. The total mean score of midwives from the Artificial Intelligence Anxiety Scale was 47.07 ± 12.10 in Generation X, 43.91 ± 12.63 in Generation Y, and 36.16 ± 12.61 in Generation Z ( P < .05), and the difference between the groups was significant. Generation X had a higher level of artificial intelligence anxiety than Generation Y, and Generation Y had higher levels of artificial intelligence than Generation Z. The total mean score of midwives from the Medical Artificial Intelligence Readiness Scale was 67.43 ± 14.28 in Generation X, 66.78 ± 17.83 in Generation Y, and 74.73 ± 16.15 in Generation Z ( P < .05), and the difference between the groups was significant. Generation Z is more ready for medical artificial intelligence than Generation X, and Generation X is more ready for medical artificial intelligence than Generation Y. In addition, in the regression analysis, there was a weakly negative and significant relationship between the mean scores of Artificial Intelligence Anxiety Scale and Medical Artificial Intelligence Readiness Scale in Generation Z midwives, and as artificial intelligence anxiety levels increased, medical artificial intelligence readiness levels decreased. The artificial intelligence anxiety levels of midwives differed by generation, being highest in Generation X and lowest in Generation Z, and the level of medical artificial intelligence readiness was highest in Generation Z and lowest in Generation Y. As artificial intelligence anxiety increased in Generation Z midwives, the level of medical artificial intelligence readiness decreased.

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助产士关于人工智能的焦虑和准备水平之间的关系:一项代际比较研究。
本研究旨在比较X代、Y代和Z代助产士对人工智能的焦虑和准备程度,并调查各代助产士对人工智能的焦虑和准备程度之间的关系。这项研究是横断面的,并与在土耳其东部一个省工作的218名助产士的研究样本进行比较。数据通过“个人信息表”、“人工智能焦虑量表”和“医疗人工智能准备量表”收集。使用IBM SPSS Statistics version 22.0 (IBM Inc., Armonk, NY, USA)软件包程序对数据进行评估。采用描述性统计、单因素方差分析、Pearson相关、回归分析等方法对数据进行分析。X代助产士人工智能焦虑量表总平均得分为47.07±12.10分,Y代为43.91±12.63分,Z代为36.16±12.61分(P < 0.05),组间差异有统计学意义。X代助产士的人工智能焦虑水平高于Y代,Y代助产士的人工智能水平高于Z代。医学人工智能准备程度量表(Medical artificial intelligence Readiness Scale)中,X代助产士的总平均得分为67.43±14.28分,Y代助产士为66.78±17.83分,Z代助产士为74.73±16.15分(P < 0.05),组间差异有统计学意义。Z世代助产士对医疗人工智能的准备程度高于X世代,X世代助产士对医疗人工智能的准备程度高于y世代。此外,在回归分析中,Z世代助产士的人工智能焦虑量表和医疗人工智能准备量表的平均得分呈弱负显著关系,随着人工智能焦虑水平的提高,医疗人工智能准备水平下降。助产士的人工智能焦虑水平随年龄的不同而不同,X一代最高,Z一代最低,Z一代医疗人工智能准备水平最高,y一代最低。随着Z一代助产士人工智能焦虑的增加,医疗人工智能准备水平下降。
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来源期刊
Cin-Computers Informatics Nursing
Cin-Computers Informatics Nursing 工程技术-护理
CiteScore
2.00
自引率
15.40%
发文量
248
审稿时长
6-12 weeks
期刊介绍: For over 30 years, CIN: Computers, Informatics, Nursing has been at the interface of the science of information and the art of nursing, publishing articles on the latest developments in nursing informatics, research, education and administrative of health information technology. CIN connects you with colleagues as they share knowledge on implementation of electronic health records systems, design decision-support systems, incorporate evidence-based healthcare in practice, explore point-of-care computing in practice and education, and conceptually integrate nursing languages and standard data sets. Continuing education contact hours are available in every issue.
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