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    The Effect of Mobile Learning on Student Success and Anxiety in Teaching Genital System Anatomy
    (Wiley, 2022) Bolatli, Gunes; Kizil, Hamiyet
    The widespread use of smartphones has led to the emergence of new mobile learning tools. The aim of this study was to compare traditional methods to mobile learning applications, and their effect on the academic achievement and anxiety levels of students learning genital system anatomy. This research study was a randomized controlled study conducted with students who took anatomy between November and December 2018. The cohort consisted of 63 students who met the sampling criteria. Groups (control = 31, experimental = 32) were randomly selected using a simple number table. The mobile application developed for the experimental group was installed on the students' mobile devices with the extension genitalsystem.apk. The anatomy of the genital system was taught to the control group using the standard curriculum and to the experimental group using the mobile application. After teaching the anatomy of the genital system, the state anxiety levels of the students in the control group were determined to be higher at 45.6 (+/- 8.7) than the experimental group at 40.4 (+/- 8.3) as measured by the 20-80 point STAI scale. The posttest examination average of the control group was 8.9 (+/- 6.9) out of 22 or 40.4 (+/- 6.9)% and the posttest average of the experimental group using mobile application was 14.9 (+/- 5.5) or 67.7 (+/- 5.5)%. State anxiety levels and examination grades showed a highly significant difference in favor of the experimental group. These results indicate that using mobile applications when teaching anatomy may be an effective method to enhance learning and reduce anxiety levels when compared to the traditional teaching methods.
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    The Effects of Bed Bathing on Vital Signs and Oxygen Saturation in Children Who Are Connected to Mechanical Ventilation
    (Lippincott Williams & Wilkins, 2018) Kizil, Hamiyet; Sendir, Merdiye
    Aim This study is a quasi-experimental research that was conducted to evaluate the effects of bathing on vital signs and oxygen saturation in intubated children who are connected to mechanical ventilation. Methods The study sample consisted of children who are treated in the pediatric intensive care unit, University of Istanbul Cerrahpasa Faculty of Medicine Hospital. A total of 60 children who met the criteria of the sample group were included in the study. The children were given bed bathing with plain warm water of 32 degrees C to 38 degrees C on 3 different days. The vital signs and oxygen saturation values of the children were measured before bathing, just after bathing, and 30 minutes after bathing them. Results Most of the children (65%) were on mechanical ventilation because of respiratory system diseases; 91.7% of them were connected to mechanical ventilation with endotracheal tube. The first bed bathing of children was given mean of 1.54 3.57 days after their hospitalization, and bathing durations were mean of 18.3 minutes. The vital signs (pulse, blood pressure, body temperature) were compared before and after bed bathing, and it was observed that the values before bathing, just after bathing, and 30 minutes after bathing had advanced level of difference (P < .001). The lowest values of all signs except body temperature were obtained 30 minutes after bed bathing. When oxygen saturation measurement values before bathing (94.5%) and after bathing were compared, it was found that the highest values (97.3%) were obtained 30 minutes after bed bathing. Conclusion The bed bathing positively affected the vital signs and oxygen saturation values in intubated children connected to the mechanical ventilation. Yet, there is still a need for more research to test the effects of bed bathing on respiratory and circulatory function.
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    PREDICTION AND CLASSIFICATION OF PRESSURE INJURIES BY DEEP LEARNING
    (Termedia Publishing House Ltd, 2021) Yilmaz, Atinc; Kizil, Hamiyet; Kaya, Umut; cakir, Ridvan; Demiral, Melek
    Pressure injuries are a serious medical problem that both negatively affects the patient's quality of life and results in significant healthcare costs. In cases where a patient doesn't receive appropriate treatment and care, death may result. Nurses play critical roles in the prevention, care, and treatment of pressure injuries as members of the healthcare team who closely monitor the health status of the patient. Today, the use of artificial intelligence is becoming more prevalent in healthcare, as in many other areas. Artificial intelligence is a method that aims to solve complex problems by using computers to mathematically simulate the way the brain works. In this article, we compile and share information about a deep learning model developed for the detection and classification of pressure injuries. Deep learning can operate on many types of data. Convolutional Neural Networks (CNN) prefer images because they can handle 2D arrays. In this case, the images, annotated according to the National Pressure Injury Advisory Panel pressure injury classification system, have been fed into a deep learning model using CNN. The developed CNN model has a 97% success in detecting and classifying pressure injuries, and as more images are collected and fed into the CNN, the prediction accuracy will increase. This deep learning model allows for the automatic detection and classification of pressure injuries, an indicator of health outcomes, at an early stage and for quick and accurate intervention. In this context, it is expected that the quality of nursing care will increase, the prevalence of pressure injury will decrease, and the economic burden of this health problem will decrease.