Objectives:
To evaluate mothers’ attitudes toward readiness for discharge care at home for a premature baby in Intensive Care Unit at teaching hospitals in Medical City Complex and to find out the relationship between mothers’ attitudes and their socio-demographic characteristics.
Methodology: A quasi-experimental study design was carried out through the period of 6th January 2020 to 2021 to 11th March 2021, to evaluate mother’s attitude toward discharge care plan for premature babies. The study carried out in Welfare Teaching Hospital, Nursing Home Hospital and Baghdad Teaching Hospital at Medical City Complex in Baghdad City on 30 mother of premature babies in neonatal intensive care units using the nonprobability sampling technique. The data was analyzed through the use of descriptive and inferential statistical data analysis approaches.
Results : The study findings indicated that most of the mothers are at age (23-29) years old, graduated from secondary schools, housewives, urban resident, and they had cesarean section delivery, they spend days at the NICU, with moderate level of socioeconomic status. Mothers had negative attitude at pretest mean score is (1.57), and at posttest mean score is (2.78) at high level of attitude toward readiness for discharge care at home for a premature baby in Intensive Care Unit.
Conclusion: The study concluded that the educational program has proved its effectiveness in improving mother’s attitudes toward readiness for discharge care at home for a premature baby in Intensive Care Unit.
Recommendations The study recommend for Training session for the nurses about discharge care plan and improves the attitudes of mothers to implementation that attitudes of mother to care of the premature babies at home.
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Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an ob
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