Research Article
Year 2023,
Volume: 12 Issue: 4, 1846 - 1854, 26.12.2023
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.
References
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- 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
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- 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
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- 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
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- 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.
Year 2023,
Volume: 12 Issue: 4, 1846 - 1854, 26.12.2023
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
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.
There are 24 citations in total.
Details
| Primary Language | English |
|---|---|
| Subjects | Health Care Administration |
| Journal Section | Original Article |
| Authors | |
| Publication Date | December 26, 2023 |
| Published in Issue | Year 2023 Volume: 12 Issue: 4 |
