Evaluation of artificial intelligence anxiety status of generation Z candidate nurses using machine learning in perspective of leadership
Vol 9, Issue 7, 2024, Article identifier:
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Abstract
This study aims to determine the artificial intelligence (AI) anxiety levels of Z-generation candidate nurses and the variables affecting the anxiety levels of artificial intelligence by the machine learning (ML) method. Data were collected from 431 candidate nurses by questionnaire using the convenience sampling method. R open access programming language was used for the statistical analysis of the study and the evaluation of significant variables according to their importance levels. The Boruta algorithm, a machine learning method, was used in the determination of the variables affecting the level of artificial intelligence anxiety according to the degree of importance. The findings showed that the most important variable for students' artificial intelligence anxiety level is age. Moreover, there is a statistically significant relationship between students' class and their anxiety level, a significant relationship between artificial intelligence and machine learning in health and their anxiety level, and a significant relationship between gender and technological competence evaluation. Furthermore, nearly half of the participants (48.5%) had very low anxiety, 12.8% had low anxiety, 30.2% had medium anxiety, 6.5% had high-level anxiety and 2.1% of them had very high levels of anxiety. With this research, the artificial intelligence anxiety of generation Z was determined by determining the demographic characteristics that are effective in artificial intelligence. We concluded that more sensitive analysis and different results can be obtained when using a machine learning algorithm compared to classical statistical analysis in determining the complex relationships in the data.
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1. Watson D, Womack J, Papadakos. Rise of the robots: Is artificial intelligence a friend or foe to nursing practice?.Critical Care Nursing Quarterly 2020; 43(3): 303-311.
2. Maalouf N, Sidaoui A, Elhajj IH, Asmar D. Robotics in nursing: a scoping review. Journal of Nursing Scholarship 2018; 50(6): 590-600.
3. Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future healthcare journal 2019; 6(2): 94.
4. Güzel Ş, Dömbekci HA, Eren F. Yapay Zekânın Sağlık Alanında Kullanımı: Nitel Bir Araştırma. Celal Bayar Üniversitesi Sağlık Bilimleri Enstitüsü Dergisi 2022; 9(4): 509-519.
5. Filiz E, Güzel Ş, Şengül A. Sağlık profesyonellerinin yapay zekâ kaygı durumlarının incelenmesi, Journal of Academic Value Studies 2022; 8(1): 47-55.
6. Higgins O, Short BL, Chalup SK, Wilson RL. Artificial intelligence (AI) and machine learning (ML) based decision support systems in mental health: An integrative review. International Journal of Mental Health Nursing 2023 doi: 10.1111/inm.13114.
7. Almaiah MA, Alfaisal R, Salloum SA, Hajjej F, Thabit S, El-Qirem FA, Al-Maroof RS. Examining the impact of artificial intelligence and social and computer anxiety in e-learning settings: students’ perceptions at the university level. Electronics 2022; 11(22): 3662.
8. Nasreldin Othman W, Mohamed Zanaty M, Mohamed Elghareeb S. Nurses' Anxiety level toward Partnering with Artificial Intelligence in Providing Nursing Care: Pre&Post Training Session. Egyptian Journal of Health Care 2021; 12(4): 1386-1396.
9. Buchanan C, Howitt ML, Wilson R, Booth RG, Risling T, Bamford M. Predicted influences of artificial intelligence on nursing education: Scoping review. JMIR nursing 2021; 4(1): e23933.
10. Miranda J, Navarrete C, Noguez J, Molina-Espinosa JM, Ramírez-Montoya MS, Navarro-Tuch SA, Molina A. The core components of education 4.0 in higher education: Three case studies in engineering education. Computers & Electrical Engineering 2021; 93: 107278.
11. Markus AF, Kors JA, Rijnbeek PR. The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies. Journal of Biomedical Informatics 2021; 113: 103655.
12. Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism 2017; 69: 36-40.
13. Briganti G, Le Moine O. Artificial intelligence in medicine: today and tomorrow. Frontiers in medicine 2020; 27(7). doi.org/10.3389/fmed.2020.00027
14. Taş D, Turanlıgil F. Sağlik Çalişanlarinin Bilgisayar Teknolojisine Karşi Tutumlari İle Teknoloji Öz-Yeterliği Düzeylerinin İşgücü Devrine Etkisi: Gaziantep Üniversitesi Tip Fakültesi Hastanesi Örneği. Anadolu Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 2020; 21(2): 1-17.
15. Kardaş Özdemir F, Karakaya G. The use of computer and information technology by nurses. The Journal of Tepecik Education and Research Hospital 2017; 27(2): 126-130. DOI: 10.5222/terh.2017.126
16. Kolcu GK, Özceylan G, Başer A, Altuntaş SB. Yapay Zekâ Kaygısı Ölçeğinin Aile Hekimlerinde Geçerlik ve Güvenirliğinin Değerlendirilmesi. Research Journal of Biomedical and Biotechnology 2021; 2(1): 20-28.
17. Akkaya B, Özkan A, Özkan H. Yapay Zeka Kaygı (YZK) Ölçeği: Türkçeye Uyarlama, Geçerlik ve Güvenirlik Çalışması. Alanya Akademik Bakış 2021; 5(2): 1125-1146. https://doi.org/10.29023/alanyaakademik.833668.
18. Wang YY, Wang YS. Development and validation of an artificial intelligence anxiety scale: an initial application in predicting motivated learning behavior. Interactive Learning Environments 2019; 30(4): 619-634. https://doi.org/10.1080/10494820.2019.1674887
19. Çetin C, Karalar S. X, Y ve Z kuşağı öğrencilerin çok yönlü ve sınırsız kariyer algıları üzerine bir araştırma. Yönetim Bilimleri Dergisi 2016; 14(28): 157-197.
20. Erten P. Z kuşağının dijital teknolojiye yönelik tutumları. Gümüşhane Üniversitesi Sosyal Bilimler Dergisi 2019; 10(1): 190-202.
21. Karadoğan A. Z kuşağı ve öğretmenlik mesleği. Ağrı İbrahim Çeçen Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 2019; 5(2): 9-41.
22. Gümüş N. Z kuşağı tüketicilerin satın alma karar tarzlarının incelenmesi. Yaşar Üniversitesi E-Dergisi 2020; 15(58): 381-396.
23. Gürdin B.Türkiye’de Robonomi: Z Kuşağı Gençlerin Hastanelerde Potansiyel Hizmet Robotu Kullanımına Yönelik Tutumları. Artuklu Kaime Uluslararası İktisadi ve İdari Araştırmalar Dergisi 2020; 3(1): 41-55.
24. Lazányi K. Generation Z and Y–are they different, when it comes to trust in robots? In 2019 IEEE 23rd International Conference on Intelligent Engineering Systems (INES) (pp. 000191-000194). IEEE).
25. Kursa MB, Rudnicki WR. Feature Selection with the Boruta Package. Journal of Statistical Software 2010; 36(11): 1–13. https://doi.org/10.18637/jss.v036.i11).
26. Park I, Kim D, Moon J, Kim S, Kang Y, Bae S. Searching for new technology acceptance model under social context: Analyzing the determinants of acceptance of intelligent information technology in digital transformation and implications for the requisites of digital sustainability. Sustainability 2022; 14(1): 579.
27. Kaya F, Aydin F, Schepman A, Rodway P, Yetişensoy O, Demir Kaya M. The Roles of Personality Traits, AI Anxiety, and Demographic Factors in Attitudes toward Artificial Intelligence. International Journal of Human–Computer Interaction 2022; 1-18.
28. Smith, J. (2019). The Impact of Gender on Artificial Intelligence Anxiety. Journal of Technology and Society, 15(2), 45-58.
29. Jones, A., Smith, B., & Johnson, C. (2020). Understanding Gender Differences in Artificial Intelligence Anxiety. Journal of Psychology and Technology, 25(3), 102-115.
30. Brown, L. (2018). Exploring the Relationship between Gender and Artificial Intelligence Anxiety. Technology and Society Review, 12(4), 231-245.
31. Garcia, P., Rodriguez, M., & Martinez, E. (2021). Gender Roles and Expectations in Relation to Artificial Intelligence Technologies. Journal of Gender Studies, 30(1), 45-58.
32. Menekli T, Şentürk S. The relationship between artificial intelligence concerns and perceived spiritual care in internal medicine nurses. YOBÜ Sağlık Bilimleri Fakültesi Dergisi 2022; 3(2): 210-218.
33. Yazdani M, Rezaei S, Pahlavanzadeh S. The effectiveness of stress management training program on depression, anxiety and stress of the nursing students. Iranian journal of nursing and midwifery research 2010; 15(4): 208-215.
34. Ramadan E, Ahmed H. The effect of health educational program on depression, anxiety and stress among female nursing students at Benha University. IOSR Journal of Nursing and Health Science 2015; 4(3): 49-56.
35. Masayuki M. The Effects of Artificial Intelligence and Robotics on Business and Employment: Evidence from a survey on Japanese firms. Res. Inst. Econ. Trade Ind 2016; 16.
36. Ma Y, Siau KL. Artificial intelligence impacts on higher education. MWAIS Proceedings 2018; 42(5): 1-5.
37. Swan BA Assessing the Knowledge and Attitudes of Registered Nurses about Artificial Intelligence in Nursing and Health Care. Nursing Economic$ 2021; 39(3): 139-143.
38. Ronquillo CE, Peltonen LM, Pruinelli L, Chu CH, Bakken S, Beduschi A, Topaz M. 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 2021; 77(9): 3707-3717.
39. Taşçı G, Çelebi M. Eğitimde yeni bir paradigma: “Yükseköğretimde yapay zekâ”. OPUS Uluslararası Toplum Araştırmaları Dergisi 2020; 16(29): 2346-2370.
40. Dobrowolski, Z., Drozdowski, G., & Panait, M. (2022). Understanding the impact of Generation Z on risk management—A preliminary views on values, competencies, and ethics of the Generation Z in public administration. International Journal of Environmental Research and Public Health, 19(7), 3868.
41. Ndou, V., Hysa, E., Ratten, V., & Ndrecaj, V. (2023). Digital transformation experiences in the Balkan countries. The Electronic Journal of Information Systems in Developing Countries, 89(2), e12262.
42. Panait, M., Ionescu, R., Apostu, S. A., & Vasić, M. (2022). Innovation through Industry 4.0-Driving Economic Growth and Building Skills for Better Jobs. Economic Insights-Trends & Challenges, (2).
DOI: https://doi.org/10.59429/esp.v9i7.6136
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