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Investigation the relationship between xenophobic attitude and intercultural sensitivity level in nurses
Metin Yıldız1, Mehmet Salih Yıldırım2, Ayşe Elkoca3
1Department of Nursing, Sakarya University, Sakarya, Turkey.
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 (β = -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.
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