This study sought to assess newborn feeding knowledge and attitudes among medical students. A sample of 649 Portuguese medical students completed an online survey containing a sociodemographic questionnaire, the Newborn Feeding Ability Questionnaire (NFA), and the Iowa Infant Feeding Attitudes Scale (IIFAS). The overall sample showed moderate scores for all variables. Gender analysis identified significant differences only for the dimension related to the benefits of skin-to-skin contact between mother and newborn where women scored higher. Analysis by year of training found that students with more years of training scored higher on all variables of newborn feeding knowledge that were positively correlated and were positive predictors of newborn feeding attitudes. Students with fewer years of training scored higher on work practices interfering with newborn feeding ability, which were negatively correlated and were negative predictors of newborn feeding attitudes. These results demonstrate that medical students with more years of training are the most prepared, however, the moderate results of the sample raise concerns. Our results point to the importance of providing medical students with adequate knowledge in order to influence their attitudes toward newborn feeding and contribute to better working practices for future health professionals.
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http://dx.doi.org/10.3390/ejihpe13030043 | DOI Listing |
Nutrients
December 2024
National Institute of Women, Children and Adolescents Health Fernandes Figueira-Fiocruz, Rio de Janeiro 22250-020, Brazil.
Background/objectives: This study aimed to determine the percentage and duration of neutralizing antibodies against the Omicron variant in human milk after vaccination against SARS-CoV-2, considering the three different vaccine technologies approved in Brazil.
Methods: A cross-sectional study was conducted with lactating women who received the complete vaccination cycle with available vaccines (AstraZeneca, Pfizer, CoronaVac, and Janssen). The participants resided in Rio de Janeiro, and samples were collected from April to October 2022.
Nutrients
December 2024
Department of Medical and Surgical Sciences and Advanced Technologies "GF Ingrassia", University of Catania, 95123 Catania, Italy.
Background/objectives: Breast milk is a dynamic, personalized nutrition source, influenced by maternal diet, lifestyle, and environmental factors, which shape its composition and impact infant health. This review synthesizes evidence on the associations between maternal lifestyles (e.g.
View Article and Find Full Text PDFCureus
December 2024
Pediatrics/Neonatology, University of Arkansas for Medical Sciences, Little Rock, USA.
A lipoblastoma is a benign tumor of adipocytes originating from embryonic white fat and occurs in the pediatric population. Congenital lipoblastomas, however, are rare, and the incidence of these tumors in neonates is unknown. Due to their rare presentation, congenital oral lipoblastomas can, firstly, pose diagnostic challenges for the pediatrician and must be differentiated from the more commonly seen oral lesions in the newborn and other rare malignant growths.
View Article and Find Full Text PDFInt Breastfeed J
January 2025
Instutite of Health Sciences, Istanbul Medipol University, Istanbul, Turkey.
Background: The use of Complementary Medicine Products (CMPs) has been increasing worldwide, including among breastfeeding mothers. This study aims to investigate the reasons and associated factors of CMP use among breastfeeding mothers in Turkey, alongside their attitudes and experiences.
Methods: A descriptive cross-sectional study was conducted using a self-administered, anonymous online survey between 17 December 2023 and 17 March 2024.
PLoS One
January 2025
Department of Pediatrics, Copenhagen University Hospital-North Zealand, Hillerød, Denmark.
Background: Identification of mother-infant pairs predisposed to early cessation of exclusive breastfeeding is important for delivering targeted support. Machine learning techniques enable development of transparent prediction models that enhance clinical applicability. We aimed to develop and validate two models to predict cessation of exclusive breastfeeding within one month among infants born after 35 weeks gestation using machine learning techniques.
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