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Öğe Artificial intelligence and robot nurses: From nurse managers' perspective: A descriptive cross-sectional study(Wiley, 2022) Ergin, Eda; Karaarslan, Duygu; Şahan, Seda; Yücel, Şebnem ÇınarAim This research was planned to identify nurse managers' opinions on artificial intelligence and robot nurses. Background As the concepts of artificial intelligence and robot nurses are becoming widespread in Turkey, nurse managers are expected to guide and cooperate with nurses in the future in regard to these technologies. Methods The sample of the study consisted of 326 manager nurses, who were reached via the online questionnaire during the period of September to November 2021. A Nurse Managers Information Form and a Question Form on Artificial Intelligence and Robot Nurses were used to collect data. Data in this cross-sectional descriptive study were collected between September 2021 and November 2021 by the online survey method. The descriptive statistics of the data were analysed with numbers and percentages. The difference between the knowledge of artificial intelligence and robot nurses and demographic characteristics was analysed with the chi-square test. Results According to the findings, 66.9% of the nurse managers reported having heard the concepts of artificial intelligence and robot nurses previously. 67.2% stated that they thought that robot nurses would benefit the nursing profession, but 86.2% voiced disbelief that robots would replace nurses. Conclusions The majority of the participating nurse managers reported that artificial intelligence and robot nurses would not replace nurses but would be beneficial for nurses and would reduce their workload. Implications for Nursing Management It should be ensured that the nurse managers plan the areas in the hospital where artificial intelligence and robot nurses will be used and determine the possible risks. Awareness should be increased with in-service trainings, and patient safety and ethical problems regarding the use of artificial intelligence and robot nurses should be identified.Öğe Can artificial intelligence and robotic nurses replace operating room nurses? The quasi-experimental research(Springernature, 2023) Ergin, Eda; Karaarslan, Duygu; Sahan, Seda; Bingol, UmranOperating room nurses, who have an important place in the professional healthcare team, should be able to use technology effectively and adapt to innovations. This study is expected to shed light on how effective the development of robotic technologies and artificial intelligence and their integration into operating room nursing will be in fulfilling the requirements of contemporary nursing philosophy. This study was designed as a single group pre- and post-test quasi-experimental study. The quasi-experimental (pretest-posttest) research design was utilized to conduct the study in a Training and Research Hospital in Western Turkey. The nurses (n = 35) working in the operating room of the aforementioned hospital were included in the study. In this study, we aimed to determine whether operating room nurses experienced anxiety due to the use of artificial intelligence and robotic nurses, and the effectiveness of the training given to them in order to raise their awareness. The following three tools were used for data collection: The Nurses' Descriptive Characteristics Form, Artificial Intelligence Knowledge Questionnaire, and Artificial Intelligence Anxiety Scale. Data extraction and analysis were performed in a narrative and tabular way. According to this study, the training given to the operating room nurses significantly increased their knowledge levels about artificial intelligence and robotic nurses, and increased their artificial intelligence- and robotic nurse-related anxiety significantly (p < 0.05). The participating operating room nurses experienced limitations regarding current information, training programs and learning opportunities on robotic surgery. We recommend that the operating room nurses should be provided with trainings on artificial intelligence technologies and robotic nurses, and that they should be enabled to use these information technologies regarding future technologies actively.Öğe How does training given to pediatric nurses about artificial intelligence and robot nurses affect their opinions and attitude levels? A quasi-experimental study(Elsevier Science Inc, 2024) Karaarslan, Duygu; Kahraman, Ayse; Ergin, EdaPurpose: This study was conducted to investigate the effect of training provided to pediatric nurses on their knowledge and attitude levels about artificial intelligence and robot nurses. Design and methods: In this study, a single -group pre- and post-test quasi -experimental design was used. Data were collected from pediatric nurses working in Training and Research Hospital located in western Turkey. Forty-three pediatric nurses participated in the study. The study data were collected using the Pediatric Nurses' Descriptive Characteristics Form , Artificial Intelligence Knowledge Form , and Artificial Intelligence General Attitude Scale . Results: The mean scores of the participating pediatric nurses obtained from the Artificial Intelligence Knowledge Form before, right after and one month after the training were 41.16 +/- 14.95, 68.25 +/- 13.57 and 69.06 +/- 13.19, respectively. The mean scores they obtained from the Positive Attitudes towards Artificial Intelligence subscale of the Artificial Intelligence General Attitude Scale before and after the training were 3.43 +/- 0.54 and 3.59 +/- 0.60, respectively whereas the mean scores they obtained from its Negative Attitudes towards Artificial Intelligence subscale were 2.68 +/- 0.67 and 2.77 +/- 0.75, respectively. Conclusions: It was determined that the training given to the pediatric nurses about artificial intelligence and robot nurses increased the nurses' knowledge levels and their artificial intelligence attitude scores, but this increase in the artificial intelligence attitude scores was not significant. Practice implications: The use of artificial intelligence and robotics or advanced technology in pediatric nursing care can be fostered. (c) 2024 Elsevier Inc. All rights reserved.Öğe The Effect of White Noise and Recorded Lullaby during Breastfeeding on Newborn Stress, Mother's Breastfeeding Success, and Comfort: A Randomized Controlled Trial(W.B. Saunders, 2025) Karakuş Selçuk, Aslı; Karaarslan, Duygu; Ergin, Eda; Salğin, EminePurpose: The study was aimed at determining the effect of exposure to white noise and recorded lullaby during breastfeeding on newborn stress, mother's breastfeeding success, and comfort. Design and methods: This single-blinded and three-parallel group randomized controlled study consisted of mothers who had given birth and whose newborns were at a hospital in Turkey. Seventy-five participants completed the study. They were assigned to white noise, lullaby, and control groups in equal numbers. Each group included 25 women. The questionnaires included descriptive characteristics of mothers, the LATCH Breastfeeding Assessment Tool, the Newborn Stress Scale, and the Visual Analog Scale. During the breastfeeding process, the newborn and mother listened to white noise and lullaby for approximately 15 min. The newborn stress and mothers' level of comfort were assessed before and after the interventions. The breastfeeding success of the mother assessed with the LATCH was completed within the first 15 min of breastfeeding. Results: The mean scores obtained from the Newborn Stress Scale by the mothers in the lullaby group and white noise group decreased significantly after the intervention. A significant difference was also observed in the control group. There were also significant differences between the changes in the mothers' comfort level of all groups. There were significant differences between the changes in the LATCH scores of the groups. Conclusion: Both white noise and recorded lullaby significantly reduced newborn stress, and increased mother's comfort in the early postpartum period. Practice implications: Music should be used as a nonpharmacological method to improve breastfeeding success in newborns. © 2025 Elsevier Inc.
