Artificial Intelligence Anxiety of Nurses and Related Factors

Year 2023, Volume: 12 Issue: 4, 1846 - 1854, 26.12.2023

Asuman Çobanoğlu , Hatice Oğuzhan

https://doi.org/10.37989/gumussagbil.1274522

Abstract

The research was carried out to examine the artificial intelligence anxiety levels of nurses and their affecting factors. In this study, a descriptive and cross-sectional design was used. The sample of the study consisted of 120 nurses (n=120). The research data were collected between 10 July and 10 October 2021. Data were collected using a Nurse Information From (NIF) and Artificial Intelligence Anxiety Scale (AIAS). Mann- Whitney U and Kruskal-Wallis test were used in the analysis of research data. The average age of nursing was 31.05±7.40 and 82.2% were females. The AI levels of the nurses were found to be 43.36±11.13. It was determined that there was a difference between the educational status of the nurses, their knowledge of AI technologies, the effect of AI technologies in patient care and their AI anxiety levels (p<0.05). This study determined that AI anxiety was higher in nurses who had a lower education level, did not have knowledge about AI technologies, and thought that AI technologies would not have a positive effect on patient care.

Keywords

Artificial intelligence, Artificial intelligence anxiety, Nurse

Hemşirelerin Yapay Zekâ Kaygısı ve İlişkili Faktörler

Abstract

Çalışma, hemşirelerin yapay zeka kaygı düzeylerini ve etkileyen faktörleri incelemek amacıyla yapılmıştır. Bu çalışmada tanımlayıcı ve kesitsel araştırma deseni kullanılmıştır. Araştırmanın örneklemini 120 hemşire oluşturmuştur (n=120). Araştırma verileri 10 Temmuz-10 Ekim 2021 tarihleri arasında toplanmıştır. Veriler “Hemşire Bilgi Formu (HBF)” ve “Yapay Zeka Kaygı Ölçeği (YZKÖ)” ile toplanmıştır. Verilerin analizinde Mann- Whitney U and Kruskal-Wallis testi kullanılmıştır. Hemşirelerin yaş ortalaması 31,05±7,40 olup %82,2’si kadındır. Hemşirelerin YZKÖ puan ortalaması 43,36±11,13 olarak bulundu. Hemşirelerin eğitim durumları, yapay zeka teknolojilerine ilişkin bilgileri, yapay zeka teknolojilerinin hasta bakımına etkisi ile yapay zeka kaygı düzeyleri arasında anlamlı bir fark olduğu belirlendi (p<0,05). Bu çalışmada, eğitim düzeyi düşük, yapay zeka teknolojileri hakkında bilgisi olmayan ve yapay zeka teknolojilerinin hasta bakımına olumlu bir etkisi olmayacağını düşünen hemşirelerin yapay zeka kaygısının daha yüksek olduğu belirlenmiştir.

Keywords

Hemşire, Yapay zeka, Yapay zeka kaygısı

References

• 1. McCarthy, J. What is Artificial Intelligence? Stanford University. Retrieved from 1956. http://jmc.stanford.edu/artificial-intelligence/whatis-ai/index.html
• 2. McGrow, K. (2019). “Artificial Intelligence”. Nursing. 49 (9), 46-49. https://doi.org/10.1097/01.NURSE.0000577716.5705
• 3. Ronquillo, C.E, Peltonen, L.M, Pruinelli L, Chu, C.H, Bakken, S, Beduschi A, Faan KC, Faan N.H, Junger, A and Michalowski, M. (2021). “Artificial intelligence in nursing: Priorities and opportunities from an international invitational think-tank of the Nursing and Artificial Intelligence Leadership Collaborative”. Journal of Advanced Nursing. 77 (9), 3707–3717. https://doi.org/10.1111/ jan.14855
• 4. Akalın, V. and Veranyurt, Ü. (2020). “Digitalization in health and artificial intelligence”. SDU Healthcare Management Journal. 2 (2), 131-141.
• 5. Akkaya, B, Özkan A, and Özkan, H. (2021). “Artificial intelligence anxiety (AIA) scale: adaptation to Turkish, validity and reliability study”. Alanya Akademic Review Journal. 5 (2), 1125-1146. https://doi.org/10.29023/alanyaakademik.833668
• 6. McKinsey Global Institute (MGI). (2017). Executive summary. In Jobs lost, jobs gained: Workforce transitions in a time of automation (p. 9). New York, NY: McKinsey and Company.
• 7. Scherer, M.U and Harv, J.L. (2015). “Regulating artificial intelligence systems: Risks, challenges, competencies, and strategies”. Harward Journal of Law and Technology. 29, 353.
• 8. Zhao, F, Egelman, S, Weeks, H.M, Kaciroti, N, Miller, A.L and Radesky, J.S. (2020). “Data collection practices of mobile applications played by preschool-aged children”. JAMA Pediatrics. 174 (12), e203345-e203345 https://doi.org/10.1001/jamapediatrics.2020.3345
• 9. Nyholm, S. and Smids, J. (2016). “The ethics of accident-algorithms for self driving cars: an applied trolley problem?”. Ethical Theory & Moral Practice. 19 (5), 1275-1289. https://doi.org/10.1007/s10677-016-9745-2
• 10. Lu, H, Li, Y, Chen, M, Kim, H. and Serikawa, S. (2018). “Brain intelligence. Go beyond artificial intelligence”. Mobile Network and Applications. 23, 368-375. https://doi.org/10.1007/s11036-017-0932-8
• 11 Leavy, S. (2018). Gender bias in artificial intelligence: The need for diversity and gender theory in machine learning. In Proceedings of the 1st international workshop on gender equality in software engineering. 14-16. https://doi.org/10.1145/3195570.3195580
• 12. Khasawneh, O.Y. (2018). “Technophobia without boarders: The influence of technophobia and emotional intelligence on technology acceptance and the moderating influence of organizational climate”. Computers in Human Behavior. 88, 210 - 218.
• 13. Frith, K.H. (2019). “Artificial intelligence: What does it mean for nursing?”. Nurse Education Perspectives. 40 (4), 261. https://doi.org/10.1097/01.NEP.0000000000000543
• 14. Seren İnteperler, Ş, Gül G. and Akbaş, E. (2022). "Artificial Intelligence Anxiety of Nurses and Associated Factors," 2022, 2. Ulusal, 1. Uluslararası Hemşirelikte Yönetim Kongresi (pp.44). İzmir, Turkey.
• 15. Gümüş, E. and Uysal Kasap, E. (2022). “Sağlık Ekosisteminde Yapay Zeka Kaygı Düzeyi; Hemşire Örneklemi”. Sağlık Bilimlerinde Yapay Zeka Dergisi, 2 (3), 1-7. https://doi.org/10.52309/jaihs.v2i2.43
• 16. Menekli, S. and Şentürk, T. (2022). “The Relationship Between Artificial İntelligence Concerns İn İnternal Medicine Nurses”. YOBU Faculty of Health Sciences Journal. 3 (2), 210-218.
• 17. Wang, Y.Y. and Wang, Y.S. (2022). “Development and validation of an artificial intelligence anxiety scale: an initial application in predicting motivated learning behavior”. Interactive Learning Environments. 30 (4), 619-634. https://doi.org/10.1080/10494820.2019.1674887
• 18. Swan, B.A. (2021). “Assessing the knowledge and attitudes of registered nurses about artificial intelligence in nursing and health care”. Nursing Economics. 39 (3), 139-143.
• 19. Buchanan, C, Howitt, M.L, Wilson, R, Booth, R.G, Risling, T. and Bamford, M. (2020). “Predicted influences of artificial intelligence on the domains of nursing”. JMIR Nursing. 3 (1), e23939. https://doi.org/10.2196/23939
• 20. Ackerman, M.L., Virani, T. and Billings, B. (2017). “Digital mental health - innovations in consumer driven care”. Nursing Leadership. 30 (3), 63-72. https://doi.org/10.12927/cjnl.2018.25384
• 21. Pepito, J.A. and Locsin, R. (2019). “Can nurses remain relevant in a technologically advanced future?”. International Journal of Nursing Sciences. 6 (1), 106-110. https://doi.org/10.1016/j.ijnss.2018.09.013
• 22. Robert, N. (2019). “How artificial intelligence is changing nursing”. Nursing Management. 50 (9), 30-39. https://doi.org/10.1097/01.NUMA.0000578988.56622.21
• 23. The National Health Service Constitution (NHS). (2019). Preparing the health care workface to deliver the digital future. 1-53.
• 24. Taş, D. and Turanlıgil, F. (2020). “The effect of the health professionals attitudes to technology and the technology self-efficacy levels on turnover: the case of Gaziantep University Faculty of medicine hospital”. Anadolu Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi. 21, 1-17.