The nine stages of skin-to-skin: practical guidelines and insights from four countries.
Kajsa BrimdyrKarin CadwellKristin SvenssonYuki TakahashiEva NissenAnn-Marie WidströmPublished in: Maternal & child nutrition (2020)
Incorporating systematic evidence with clinical expertise is a key element in the quest to improve quality of care and patient outcomes. The evidence supporting skin-to-skin contact in the first hour after birth is robust and includes significantly improved outcomes for both mother and infant. This paper compares available iterative data about newborn behaviour in the first hour after birth to further describe the observable behaviour pattern and to provide clinical insight for further research. Although the evidence for positive outcomes through skin-to-skin contact are robust, there is a dearth of research specifically focused on clinical practice. The methodology considers the four available data sets that used Widström's 9 stages, which consists of studies from Japan, Sweden, Italy and the United States, examining the parameters of each stage across settings from around the world. This research provides an expanded understanding of the timing of the newborn's progression through Widström's 9 observable stages. We found that newborns in all four data sets began with a birth cry and continued through the remaining stages of relaxation, awakening, activity, rest, crawling, familiarization, suckling and sleeping during the first hours after birth and consolidated the data into a Sign of the Stages chart to assist in further research. The evidence supports making a safe space and time for this important newborn behaviour. Clinical practices should encourage and protect this sensitive period.
Keyphrases
- soft tissue
- wound healing
- electronic health record
- gestational age
- clinical practice
- big data
- healthcare
- blood pressure
- primary care
- magnetic resonance imaging
- type diabetes
- pregnant women
- palliative care
- magnetic resonance
- adipose tissue
- quality improvement
- artificial intelligence
- weight loss
- deep learning
- single molecule
- glycemic control