Investigation the relationship between xenophobic attitude and intercultural sensitivity level in nurses.

OBJECTIVE: The level of intercultural sensitivity is important for nurses to approach the individual's culture in care and treatment without prejudice. In this study, it was aimed to determine the effect of nurses' intercultural sensitivity level on xenophobic attitude. METHODS: This quantitative cross-sectional study was conducted at Van Training and Research Hospital between January and June 2022. The Introductory Information Form, the Xenophobia Scale, and the Intercultural Sensitivity Scale were used to collect the research data. SPSS-25 package program and R programming language 4.1.3 are used. RESULTS: This study was conducted with 235 nurses. According to the findings obtained in our study, the regression model determine the effect of intercultural sensitivity on xenophobia level was found to be F(1,233) = 69.857, p = 0.001, and 23.1 % (R2 = 0.231) of the variance in the dependent variable was explained by the independent variable. Intercultural sensitivity has a negative and significant effect on the level of xenophobia (ß = -0.480; t (233) = -8.358, p = 0.001). When comparing the performance of all variables with machine learning algorithms for the prediction model, the best performing algorithm was found to be Random Forest (RF). The contributions of these variables to the model were calculated with Shapley Additive Explanations (SHAP) values. The most important variables that should be included in the model to predict the xenophobia variable are the respect for cultural differences sub-dimension and intercultural sensitivity variables. CONCLUSION: It was determined that as the level of intercultural sensitivity of the nurses increased, their xenophobic attitudes decreased. Longitudinal studies on xenophobic attitude in nurses are recommended. It is recommended to make predictions with different machine learning models.

Determining the correlation between old age adaptation, aging in place and depression in older adults: A descriptive correlational study.

We conducted this study to determine the correlation between old age adaptation, aging in place, and depression in older adults. We carried out the descriptive correlational study with older adults (n:319) Agri Provincial Directorate of Health between October 2021 and April 2022. "SPSS 25 and Amos 23 programs" were used in the analysis of the data. Python 3.11.1, Microsoft Power BI programs were used for machine learning and graphical visualizations. In the study, it was determined that the model for the relationship between aging at a place level, adaptation difficulty in older adults level, and geriatric depression level was significant (F(2,316) = 37.002, p = 0.001). The aging in place level and the adaptation difficulty in older adults level explain 18.5% of the total variance of the level of geriatric depression. In the regression model, it was determined that the increase in the level of aging in place (t = -6.081, p < 0.001) and the decrease in the level of adaptation difficulty in older adults (t = 4.107, p < 0.001) of the participants caused a statistical decrease in the level of "Geriatric Depression." It has been determined that adaptation difficulty in older adults has a moderator role in the relationship between geriatric depression level and aging in place level. In our data set, while the estimate of the presence of depression was 40 % with logistic regression analysis, it was 97 % with Random Forest. The study determined that as the level of aging in -place increased and the adaptation difficulty in older adults decreased, and depression decreased.

The effect of rumination and elder abuse level on successful aging in elderly individuals: Analysis with a machine learning approach.

BACKGROUND: This study aimed to determine the effect of rumination and elder abuse levels on successful aging in elderly individuals. METHODS: This descriptive-correlational study was conducted with older individuals (n = 297) in Agri, Turkey, between October 2021 and May 2022. SPSS-22 and R v.4.1.3 were used in the study. Analysis was done with machine learning and artificial neural networks. RESULTS: In the study, the mean age of the older individuals was 67.49 ± 3.04. Statistical estimates showed the regression model to be significant and usable, F(2,294) = 116.659, P = 0.001. Elder abuse and ruminative thought levels together explained 43.9% of the total variance (R2 = 0.439) of successful aging. The regression model indicated that increases in both the elder abuse level (t = -3.672, P < 0.001) and the ruminative thought level (t = -12.771, P < 0.001) of participants caused a statistically significant decrease in the successful aging level. The combined elder abuse and ruminative thought levels had a greater effect on successful aging (Model 1: F = 45.321, Model 2: F = 116.659, R2 Change = 0.309). The R2 value of the Random Forest regression model was 0.529, showing that the independent variables could explain 52.92% of the variation in the dependent variable. According to the SHapley Additive exPlanation values, which show the contribution or importance of each variable in the estimation of the model, it was determined that the most important variables for estimating the Successful Aging Scale variable were ruminative thought style and elder abuse screening. CONCLUSIONS: Our study indicates that a decrease in the levels of rumination and elder abuse could lead to improvement in successful aging. Longitudinal studies on successful aging in older individuals are recommended.

Segmentation and classification of skin burn images with artificial intelligence: Development of a mobile application.

AIM: This study was conducted to determine the segmentation, classification, object detection, and accuracy of skin burn images using artificial intelligence and a mobile application. With this study, individuals were able to determine the degree of burns and see how to intervene through the mobile application. METHODS: This research was conducted between 26.10.2021-01.09.2023. In this study, the dataset was handled in two stages. In the first stage, the open-access dataset was taken from https://universe.roboflow.com/, and the burn images dataset was created. In the second stage, in order to determine the accuracy of the developed system and artificial intelligence model, the patients admitted to the hospital were identified with our own design Burn Wound Detection Android application. RESULTS: In our study, YOLO V7 architecture was used for segmentation, classification, and object detection. There are 21018 data in this study, and 80% of them are used as training data, and 20% of them are used as test data. The YOLO V7 model achieved a success rate of 75.12% on the test data. The Burn Wound Detection Android mobile application that we developed in the study was used to accurately detect images of individuals. CONCLUSION: In this study, skin burn images were segmented, classified, object detected, and a mobile application was developed using artificial intelligence. First aid is crucial in burn cases, and it is an important development for public health that people living in the periphery can quickly determine the degree of burn through the mobile application and provide first aid according to the instructions of the mobile application.