Purpose: Neonatal nurses are expected to have clinical competency to provide qualified and safe care for high-risk infants. An educational intervention to enhance nurses’ clinical competence is often a priority in the nursing field. This study was conducted to explore nurses’ perceived importance and performance confidence of nursing care activities in neonatal intensive care units.Methods: One hundred forty-one neonatal nurses from seven hospitals across South Korea participated in the online survey study. The scale of neonatal nursing care activity consisted of 8 subdomains including professional practice (assessment, diagnosis, planning, intervention, evaluation, education, research, and leadership). The Importance-Performance Matrix was used to analyze the importance of and confident performance in each of the nursing subdomains.Results: Both importance and performance confidence increased as nurses’ age (p=.042 and p<.001) and clinical experience (p=.004 and p<.001). Participants scored relatively higher in importance and performance confidence in the professional practice subdomains (assessment, intervention, evaluation), but scored lower in the education and research subdomains.Conclusion: To provide evidence-based nursing care for high-risk infants in neonatal intensive care units, educational interventions should be developed to support nurses based on the findings of the research.

Family caregivers of children with tracheostomies or home ventilators are more likely to experience poor sleep quality when undertaking the full responsibility of caring for fragile children. This scoping review aimed to identify the sleep quality, related factors, and their impact on the health of family caregivers of children with tracheostomies or home ventilators. The included studies ( N = 16) were retrieved through PubMed, CINAHL, Cochrane Library, Embase, PsycINFO, and Web of Science. Family caregivers’ sleep were low in quality, frequently disturbed, and insufficient. Their sleep quality was related to fatigue, anxiety, depression, family functioning, and health-related quality of life. The sleep disturbing factors were classified as child, caregiver, or environment-related, which were mutually interrelated. This review emphasizes the need to develop nursing interventions to both improve the sleep quality of family caregivers and the health of children with tracheostomies or home ventilators based on an in-depth understanding of the family’s context.

Young children are increasingly exposed to an obesogenic environment through increased intake of processed food and decreased physical activity. Mothers’ perceptions of obesity and parenting styles also influence children’s abilities to maintain a healthy weight. This study aimed to develop a prediction model for childhood obesity in 10-year-olds and to identify relevant risk factors using a machine learning method. Data on 1185 children and their mothers were obtained from the Korean national panel study. A prediction model for obesity was developed based on factors of both children (gender, eating habits, activity, and previous body mass index) and their mothers (education level, self-esteem, and body mass index). These factors were selected based on the least absolute shrinkage and selection operator. The prediction model was validated with the Area Under the Receiver Operator Characteristic Curve of 0.82 and an accuracy of 76%. Besides body mass index for both children and mothers, significant risk factors for childhood obesity were less physical activity among children and higher self-esteem among mothers. This study adds new evidence demonstrating maternal self-esteem is related to children’s body mass index. Future studies are needed to develop effective strategies for screening young children at risk for obesity, along with their mothers.

Young children are increasingly exposed to an obesogenic environment through increased intake of processed food and decreased physical activity. Mothers’ perceptions of obesity and parenting styles influence children’s abilities to maintain a healthy weight. This study developed a prediction model for childhood obesity in 10-year-olds, and identify relevant risk factors using a machine learning method. Data on 1185 children and their mothers were obtained from the Korean National Panel Study. A prediction model for obesity was developed based on ten factors related to children (gender, eating habits, activity, and previous body mass index) and their mothers (education level, self-esteem, and body mass index). These factors were selected based on the least absolute shrinkage and selection operator. The prediction model was validated with an Area Under the Receiver Operator Characteristic Curve of 0.82 and an accuracy of 76%. Other than body mass index for both children and mothers, significant risk factors for childhood obesity were less physical activity among children and higher self-esteem among mothers. This study adds new evidence demonstrating that maternal self-esteem is related to children’s body mass index. Future studies are needed to develop effective strategies for screening young children at risk for obesity, along with their mothers.

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