Background: The rising rate of cesarean deliveries has generated concern about whether all procedures are medically justified. Limited data exist on how well first-time mothers understand the indications for their cesarean section in Iraq. Objective: To assess maternal knowledge of the medical reasons for the first cesarean delivery and its relationship with sociodemographic, obstetric, and neonatal characteristics. Methods: A cross-sectional study was conducted from October to December 2023 among 158 Iraqi women who underwent their first cesarean delivery. Data were collected using a structured, self-administered online questionnaire that assessed demographic, obstetric, and knowledge-related factors. Results: Less than half of the mothers (48.1%) demonstrated good knowledge of the reason for their cesarean section, whereas 23.4% showed poor awareness. Knowledge level was significantly associated with residency (p=0.024), history of vaginal birth (p=0.001), place of delivery (p=0.001), type of cesarean section (p=0.001), and hypertension during pregnancy (p=0.025). Mothers from rural areas, those delivering in public hospitals, and those undergoing emergency procedures had the highest proportions of poor knowledge. Poor awareness was also linked with adverse neonatal outcomes, including low birth weight (p=0.036), preterm birth (p=0.003), and neonatal distress (p=0.001). Conclusions: A considerable proportion of first-time Iraqi mothers lack adequate understanding of the medical indications for their cesarean delivery. Educational interventions and improved patient–provider communication, especially in rural areas and public hospitals, are essential to promote informed decision-making and better maternal–neonatal outcomes.
Objectives: To assess the information of mothers regarding asthmatic child care, and to find out the relationship between information of mothers and some of demographic characteristic such as age of mothers, Level of education, and away of child feeding. Methodology: Quantitative design (a descriptive study) was conducted in pediatric hospital in Kirkuk city from the period of first of July 2011 to the end of March 2012. To achieve the objectives of the study, non probability sample of (50) mothers having asthmatic children who attend to the pediatric hospital. The data are collected through utilization
Background The escalating global concern over increased body weight in adolescents, coupled with the rising rates of adolescent pregnancy worldwide, presents a significant challenge to healthcare systems. We plan to identify the maternal and neonatal consequences associated with pre-pregnancy overweight in adolescent women. Methods Throughout five years, all singleton adolescent pregnant women with pre-pregnancy self-reported body mass index (BMI) of 18.5– ≤ 29.9 were involved during the first-trimester visit. Two groups were generated: overweight and appropriate-weight (BMI 25–29.9 and 18.5–24.9, respectively). Obstetric and neonatal outcomes were observed prospectively and statistically adjusted for the confounding factors.
... Show MoreBackground The escalating global concern over increased body weight in adolescents, coupled with the rising rates of adolescent pregnancy worldwide, presents a significant challenge to healthcare systems. We plan to identify the maternal and neonatal consequences associated with pre-pregnancy overweight in adolescent women. Methods Throughout five years, all singleton adolescent pregnant women with pre-pregnancy self-reported body mass index (BMI) of 18.5– ≤ 29.9 were involved during the first-trimester visit. Two groups were generated: overweight and appropriate-weight (BMI 25–29.9 and 18.5–24.9, respectively). Obstetric and neonatal outcomes were observed prospectively and statistically adjusted for the confounding factors.
... Show MoreSemantic segmentation is an exciting research topic in medical image analysis because it aims to detect objects in medical images. In recent years, approaches based on deep learning have shown a more reliable performance than traditional approaches in medical image segmentation. The U-Net network is one of the most successful end-to-end convolutional neural networks (CNNs) presented for medical image segmentation. This paper proposes a multiscale Residual Dilated convolution neural network (MSRD-UNet) based on U-Net. MSRD-UNet replaced the traditional convolution block with a novel deeper block that fuses multi-layer features using dilated and residual convolution. In addition, the squeeze and execution attention mechanism (SE) and the s
... Show MoreThis paper performance for preparation and identification of six new complexes of a number of transition metals Cr (lII), Mn (I1), Fe (l), Co (II), Ni (I1), Cu (Il) with: N - (3,4,5-Trimethoxy phenyl-N - benzoyl Thiourea (TMPBT) as a bidentet ligand. The prepared complexes have been characterized, identified on the basis of elemental analysis (C.H.N), atomic absorption, molar conductivity, molar-ratio ,pH effect study, I. Rand UV spectra studies. The complexes have the structural formula ML2X3 for Cr (III), Fe (III), and ML2X2 for Mn (II), Ni (II), and MLX2 for Co (Il) , Cu (Il).
Objectives: 1. Assessment women’s knowledge about caesarean section. 2. Determining women’s knowledge in relation to their demographic characteristics (age, level of education, and economic status). Methodology: A descriptive design was conducted on Assessment Women’ Knowledge about Cesarean Section at Maternity and Pediatric Hospital in AL-Samawa City. This study started from 26th of September 2020 up to 16th March 2021. Sample of (100) married women who were at reproductive age, pregnant (prime or multipara ) who were planned to have birth by elective cesarean section or had previous elective caesarian section without medical indication or women who had cesarean section with medical indication or emergency. Results: Results
... Show More