Evaluation of Nurse Academicians’ Knowledge, Attitudes/Behaviours, and Anxiety Levels Regarding Artificial Intelligence Applications
Öz
Aim: This study was conducted to evaluate the knowledge, attitude/behavior and anxiety levels of nurse academics about artificial intelligence applications.
Material and Methods: The research was conducted online with 202 nurse academicians in a descriptive type. Data Collection Form, Artificial Intelligence Anxiety Scale were used to collect data. SPSS 21 package program was used to evaluate the data. Descriptive statistics, Kolmogorov-Smirnov, Shapiro-Wilk, Spearman, Mann-Whitney U, Kruskal-Wallis H tests were used to evaluate the data. p<0.05 was considered significant.
Results: The study was completed with 202 nursing academicians. It was determined that the average score of the academicians on the Artificial Intelligence Anxiety Scale was 57.59±8.84. All participants stated that they had heard of the concept of artificial intelligence before. It was determined that there was a significant relationship between the academicians' receiving training on artificial intelligence, their belief that artificial intelligence will affect the nursing profession in the future, and their average score on the Artificial Intelligence Anxiety Scale.
Conclusion: It has been detected that nursing academicians have high levels of anxiety about artificial intelligence. It has been determined that academicians' anxiety levels about artificial intelligence are affected by lack of knowledge and negative attitudes. Our recommendation is to inform nursing academicians about artificial intelligence and provide the necessary support for them to take an active role in the inclusion of artificial intelligence in educational processes.
Anahtar Kelimeler
Artificial intelligence, nurse, academic, anxiety
Destekleyen Kurum
This research received no external funding.
Etik Beyan
Before starting the study, ethical approval (Date: 05.04.2023) and permission to use the Artificial Intelligence Anxiety Scale were obtained. Participants were provided information about the study through the text at the beginning of the survey questionnaire. Only voluntary participants were included in the study.
Teşekkür
We would like to thank all the nurse academicians who participated in our study.
Kaynakça
- 1. Frith KH. Artificial intelligence: What does it mean for nursing?. Nurs Educ Perspect 2019; 40(4): 261. http://dx.doi.org/10.1097/01.NEP.0000000000000543
- 2. Sendir M, Simsekoglu N, Kaya A, Sumer K. Nursing in the technology of the future. J Nurs Health Sci Univ 2019; 1(3): 209-214.
- 3. Betriana F, Tanioka R, Gunawan J, Locsin RC. Healthcare robots and human generations: Consequences for nursing and healthcare. Collegian 2022; 29(5): 767-773. https://doi.org/10.1016/j.colegn.2022.01.008
- 4. Hughes JD, Chivers P, Hoti K. The clinical suitability of an artificial intelligence-enabled pain assessment tool for use in infants: Feasibility and usability evaluation study. J Med Internet Res 2023; 25: e41992. https://doi.org/10.2196/41992
- 5. McGrow K. Artificial intelligence: Essentials for nursing. Nurs 2019; 49(9): 46. https://doi.org/10.1097/01.NURSE.0000577716.57052.8d
- 6. Buchanan C, Howitt ML, Wilson R, Booth RG, Risling T, Bamford M. Nursing in the age of artificial intelligence: Protocol for a scoping review. JMIR Res Protocols 2020; 9(4): e17490. https://doi.org/10.2196/17490
- 7. Bodur G, Dincer M, Tutak Z, Akyuz GE, Uyanik S, Kuvan D. The effects of artificial intelligence on the future of health: An example of a qualitative study from the perspective of university students. J Göbeklitepe Health Sci 2022; 5(7): 106-115. https://doi.org/10.55433/gsbd.149
- 8. Lukić A, Kudelić N, Antičević V, Lazić-Mosler E, Glunčić V, Hren D, et al. First-year nursing students’ attitudes towards artificial intelligence: Cross-sectional multi-center study. Nurs Educ Pract 2023; 71: 103735. https://doi.org/10.1016/j.nepr.2023.103735
- 9. Mathur P, Burns ML. Artificial intelligence in critical care. Int Anesthesiol Clin 2019: 57(2): 89-102. https://doi.org/10.1097/AIA.0000000000000221
- 10. Jeong GH. Artificial intelligence, machine learning, and deep learning in women's health nursing. Korean J Women Health Nurs 2020; 26(1): 5-9. https://doi.org/10.4069/kjwhn.2020.03.11