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.
Due to the significant role in understanding cellular processes, the decomposition of Protein-Protein Interaction (PPI) networks into essential building blocks, or complexes, has received much attention for functional bioinformatics research in recent years. One of the well-known bi-clustering descriptors for identifying communities and complexes in complex networks, such as PPI networks, is modularity function. The contribution of this paper is to introduce heuristic optimization models that can collaborate with the modularity function to improve its detection ability. The definitions of the formulated heuristics are based on nodes and different levels of their neighbor properties. The modulari
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The statistical methods have been used for testing the hypothesis is the mean, standard deviation correlation coefficient.
The resea
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