Ayşe Nur Yilmaz, Sümeyye Altiparmak, Remziye Sökmen
{"title":"The Relationship Between Anxiety and Readiness Levels Regarding Artificial Intelligence in Midwives: An Intergenerational Comparative Study.","authors":"Ayşe Nur Yilmaz, Sümeyye Altiparmak, Remziye Sökmen","doi":"10.1097/CIN.0000000000001269","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":50694,"journal":{"name":"Cin-Computers Informatics Nursing","volume":" ","pages":""},"PeriodicalIF":1.3000,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cin-Computers Informatics Nursing","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/CIN.0000000000001269","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
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.
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.