Determination of Artificial Intelligence Anxiety Status of Nursing Students: Cross-Sectional-Descriptive Study

Yıl 2024, , 304 - 312, 22.08.2024

Pınar Ongün , Beytullah Gül , İbrahim Enes Muslu , Mert Mete Meşe , Sibel Ergün

https://doi.org/10.46413/boneyusbad.1455856

Öz

Aim: The study aimed to determine the anxiety of nursing students about the emergence and use of artificial intelligence products.
Material and Method: The data of this descriptive and cross-sectional study were collected between 02.01.2023 and 15.04.2023. The sample of the research consisted of 243 students. The data collection tool included an introductory information form and the Artificial Intelligence Anxiety Scale. T-test, and one-way ANOVA test were used to analyze the data.
Results: 64.6% of the students had heard of artificial intelligence-supported devices used in healthcare, 54.7% thought that artificial intelligence applications were useful in ensuring patient safety, and 54.7% thought that the system would reduce the risk of making medical errors. The mean total score of the scale was 46.25 ± 9.66. There was a statistically significant relationship between thinking that artificial intelligence should be a course in education and thinking that artificial intelligence would be indispensable in surgical applications and the artificial intelligence anxiety scale (p<0.05).
Conclusion: Students' anxiety about artificial intelligence is at a moderate level. It was found that most students thought they should have courses on artificial intelligence applications and that artificial intelligence was useful in ensuring patient safety.

Anahtar Kelimeler

Artificial Intelligence, Anxiety, Nursing Student, Health Care

Hemşirelik Öğrencilerinin Yapay Zekâ Kaygı Durumlarının Belirlenmesi: Kesitsel-Tanımlayıcı Çalışma

Öz

Amaç: Araştırmada hemşirelik öğrencilerinin yapay zekâ ürünlerinin ortaya çıkması ve kullanıma sunulması ile ilgili kaygı durumlarını belirlemek amaçlandı.
Gereç ve Yöntem: Bu tanımlayıcı ve kesitsel araştırmanın verileri 02.01.2023-15.04.2023 tarihleri arasında elde edildi. Araştırmanın örneklemini 243 öğrenci oluşturdu. Veri toplama araçları arasında tanıtıcı bilgi formu ve Yapay Zekâ Kaygı Ölçeği yer aldı. Verilerin analizinde sayı yüzde, t-testi, Tek yönlü (Oneway) Anova testi kullanıldı.
Bulgular: Öğrencilerin %64.6’sı sağlık alanında kullanılan yapay zeka destekli cihazları duymuş olup %54.7’si yapay zeka uygulamalarının hasta güvenliğini sağlamada yararlı olduğunu ve %54.7’si sistemin tıbbi hata yapma riskini azaltacağını düşünmektedir. Çalışmada Yapay Zekâ Kaygı ölçeğinin toplam puan ortalamasının 46.25 ± 9.66 olduğu bulundu. Eğitimde yapay zekanın ders olması gerektiğini düşünme ve yapay zekanın cerrahi uygulamalarda vazgeçilmez olacağını düşünme ile yapay zekâ kaygı ölçeği arasında istatistiksel olarak anlamlı ilişki saptandı (p<0.05).
Sonuç: Öğrencilerin yapay zekâ kaygıları orta seviyededir. Öğrencilerin çoğunluğunun, yapay zeka uygulamaları ile ilgili derslerin olması gerektiğini ve yapay zekanın hasta güvenliğini sağlamada yararlı olduğunu düşündüğü sonucu bulundu.

Anahtar Kelimeler

Yapay Zeka, Anksiyete, Hemşirelik öğrencileri, Sağlık Bakımı

Kaynakça

• Abid, A., Awan, B., Ismail, T., Sarwar, N., Sarwar, G., Tariq, M. (2019). Artificial Intelligence: Medical students attitude ın district peshawar Pakistan. Pak J Public Health, 9(1), 19–21. doi: 10.1186/s41747-018-0061-6
• Akkaya, B., Özkan, A., Ozkan, H. (2021). Yapay Zeka Kaygı (YZK) Ölçeği: Türkçeye Uyarlama, Geçerlik ve Güvenirlik Çalışması. Alanya Akademik Bakış, 5(2), 1125-1146. doi:10.29023/alanyaakademik.833668
• Arda, M. S., Guirnaldo, S. A., Permites, I. D., Salaan, C. J. O. (2021). Object detection as a technological adjunct to the manual counting protocol during surgery. 2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2021. doi: 10.1109/HNICEM54116.2021.9731895
• Cochran, K. (2022). Guidelines in practice: prevention of unintentionally retained surgical ıtems. AORN Journal, 116(5), 427–440. doi:10.1002/aorn.13804
• Cohen, J. (1992). Statistical power analysis. Current Directions in Psychological Science, 1(3), 98–101. doi: 10.1111/1467-8721.ep10768783
• Epstein, N. (2021). A perspective on wrong level, wrong side, and wrong site spine surgery. Surgical Neurology International, 12(286). doi: 10.25259/SNI_402_2021
• Ergin, E., Karaarslan, D., Şahan, S., Bingöl, Ü. (2023). Can artificial intelligence and robotic nurses replace operating room nurses? The quasi-experimental research. In Journal of Robotic Surgery, 17(4), 1847–1855. doi: 10.1007/s11701-023-01592-0
• Ergin, E., Karaarslan, D., Şahan, S., Çınar Yücel, Ş. (2022). Artificial intelligence and robot nurses: From nurse managers’ perspective: A descriptive cross-sectional study. Journal of Nursing Management, 30(8), 3853–3862. doi: 10.1111/jonm.13646
• Hee Lee, D., Yoon, S. N. (2021). Application of artificial intelligence-based technologies in the healthcare industry: Opportunities and challenges. International Journal of Environmental Research and Public Health, 18(1), 1–18. doi: 10.3390/ijerph18010271
• Helaly, H. A., Badawy, M., Haikal, A. Y. (2023). A review of deep learning approaches in clinical and healthcare systems based on medical image analysis. Multimedia Tools and Applications. doi: 10.1007/s11042-023-16605-1
• Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., …Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. In Stroke and Vascular Neurology, 2(4), 230–243. BMJ Publishing Group. doi:10.1136/svn-2017-000101
• King, C. R., Shambe, A., Abraham, J. (2023). Potential uses of AI for perioperative nursing handoffs: a qualitative study. JAMIA Open, 6(1). doi:10.1093/jamiaopen/ooad015
• Labrague, L. J., Aguilar-Rosales, R., Yboa, B. C., Sabio, J. B., de los Santos, J. A. (2023). Student nurses’ attitudes, perceived utilization, and intention to adopt artificial intelligence (AI) technology in nursing practice: A cross-sectional study. Nurse Education in Practice, 73. doi: 10.1016/j.nepr.2023.103815
• Lei, L. (2022). Observation on the effect of ıntelligent machine-assisted surgery and perioperative nursing. In Journal of Healthcare Engineering, 2022. doi:10.1155/2022/6264441
• Loftus, T. J., Tighe, P. J., Filiberto, A. C., Efron, P. A., Brakenridge, S. C., Mohr, A. M., ... Bihorac, A. (2020). Artificial ıntelligence and surgical decision-making. In JAMA surgery, 155(2), 148–158. NLM (Medline). doi: 10.1001/jamasurg.2019.4917
• Lukić, A., Kudelić, N., Antičević, V., Lazić-Mosler, E., Glunčić, V., Hren, D., Lukić, I. K. (2023). First-year nursing students’ attitudes towards artificial intelligence: Cross-sectional multi-center study. Nurse Education in Practice, 71. doi: 10.1016/j.nepr.2023.103735
• Ng, Z. Q. P., Ling, L. Y. J., Chew, H. S. J., Lau, Y. (2022). The role of artificial intelligence in enhancing clinical nursing care: A scoping review. Journal of Nursing Management, 30(8), 3654–3674. doi:10.1111/jonm.13425
• O’Connor, S., Yan, Y., Thilo, F. J. S., Felzmann, H., Dowding, D., Lee, J. J. (2023). Artificial intelligence in nursing and midwifery: A systematic review. In Journal of Clinical Nursing, 32(13–14), 2951–2968. doi: 10.1111/jocn.16478
• Ohneberg, C., Stöbich, N., Warmbein, A., Rathgeber, I., Mehler-Klamt, A. C., Fischer, U., Eberl, I. (2023). Assistive robotic systems in nursing care: a scoping review. BMC Nursing, 22(1). doi: 10.1186/s12912-023-01230-y
• Öcal, E. E., Atay, E., Önsüz, M. F., Altın, F., Çokyiğit, F. K., Kılınç, S., ... Yiğit, F. N. (2020). Medical faculty students' thoughts on artificial ıntelligence in medicine. TÖAD, 2(1), 9-16.
• Özdemir, L., Bilgin, A. (2021). The use of artificial ıntelligence in health and ethical problems. Sağlık ve Hemşirelik Yönetimi Dergisi, 8(3), 439–445. doi:10.54304/shyd.2021.63325
• Peng, J., Ang, S. Y., Zhou, H., Nair, A. (2023). The effectiveness of radiofrequency scanning technology in preventing retained surgical items: An integrative review. In Journal of Clinical Nursing, 32(13–14), 3315–3327. doi:10.1111/jocn.16447
• Pepito, J. A., Locsin, R. (2019). Can nurses remain relevant in a technologically advanced future? In International Journal of Nursing Sciences, 6(1), 106–110. Chinese Nursing Association. doi:10.1016/j.ijnss.2018.09.013
• Pinto dos Santos, D., Giese, D., Brodehl, S., Chon, S. H., Staab, W., Kleinert, R., … Baeßler, B. (2019). Medical students’ attitude towards artificial intelligence: a multicentre survey. European Radiology, 29(4), 1640–1646. doi:10.1007/s00330-018-5601-1
• Sirihorachai, R., Saylor, K. M., Manojlovich, M. (2022). Interventions for the Prevention of Retained Surgical Items: A Systematic Review. In World Journal of Surgery, 46(2), 370–381. doi:10.1007/s00268-021-06370-3
• Soumpasis, I., Nashef, S., Dunning, J., Moran, P., Slack, M. (2023). Safe implementation of surgical innovation: a prospective registry of the Versius Robotic Surgical System. BMJ Surgery, Interventions, and Health Technologies, 5(1). doi:10.1136/bmjsit-2022-000144
• Speth, J. (2023). Guidelines in Practice: Minimally Invasive Surgery. AORN Journal, 118(4), 250–257. doi:10.1002/aorn.14001
• Teng, M., Singla, R., Yau, O., Lamoureux, D., Gupta, A., Hu, Z., Hu, R., …. Field, T. S. (2022). Health care students’ perspectives on artificial ıntelligence: countrywide survey in Canada. JMIR Medical Education, 8(1). doi: 10.2196/33390
• Vasquez, B. A., Moreno-Lacalle, R., Soriano, G. P., Juntasoopeepun, P., Locsin, R. C., Evangelista, L. S. (2023). Technological machines and artificial intelligence in nursing practice. Nursing and Health Sciences, 25(3), 474–481. doi: 10.1111/nhs.13029
• Yılmaz, Y., Uzelli Yılmaz, D., Yıldırım, D., Akın Korhan, E., Özer Kaya, D. (2021). Artificial ıntelligence and the opinions of the faculty of health sciences students on the use of artificial ıntelligence in health. SDÜ Health Sciences Journal, 12(3), 297-308. doi:10.22312/sdusbed.950372
• Wagner, L., Kolb, S., Leuchtenberger, P., Bernhard, L., Jell, A., Wilhelm, D. (2023). Deep learning assisted intraoperative instrument cleaning station for robotic scrub nurse systems. At-Automatisierungstechnik, 71(7), 572–579. doi: 10.1515/auto-2023-0062
• Weprin, S., Crocerossa, F., Meyer, D., Maddra, K., Valancy, D., Osardu, R., … Autorino, R. (2021). Risk factors and preventive strategies for unintentionally retained surgical sharps: a systematic review. Patient Safety in Surgery, 15(1), 1–10. doi:10.1186/s13037-021-00297-3
• WHO. (2021). Ethics and Governance of Artificial Intelligence for Health: WHO guidance. In World Health Organization. Retrieved 01.12.2023, from http://apps.who.int/bookorders.
• WHO. (2022a). Ageing and Health. World Health Organization. Retrieved 01.12.2023, from https://www.who.int/news-room/fact-sheets/detail/ageing-and-health
• WHO. (2022b). Ensuring artificial intelligence (AI) technologies for health benefit older people. World Health Organization. Retrieved 01.12.2023, from https://www.who.int/news/item/09-02-2022-ensuring-artificial-intelligence-(ai)-technologies-for-health-benefit-older-people