South Korean nurses' lived experiences supporting maternal postpartum bonding in the neonatal intensive care unit.

PURPOSE: Preterm birth and admission to a neonatal intensive care unit (NICU) can disrupt the parent-infant bonding relationship. Although neonatal nurses are in the best position to support maternal postpartum bonding in the NICU, few qualitative studies have described their challenges, strategies, and lived experiences. METHODS: This study aimed to explore and understand the experiences and perspectives of nurses supporting infants hospitalized in the NICU and their families in relation to the bonding process. We conducted a qualitative study using interpretive phenomenological analysis with 12 in-depth, semi-structured interviews recorded and transcribed verbatim between April and November 2018. We thematically analysed the data using NVivoTM software. RESULTS: Two themes emerged: (1) Being a bridge between separated mothers and infants (five subthemes); (2) Challenges in providing supportive care for maternal postpartum bonding in the NICU (three subthemes). CONCLUSIONS: Nurses have a variety of experiences regarding maternal postpartum bonding; however, the clinical reality of NICUs limits support for bonding formation. Although nurses face challenges (e.g., institutional policies, insufficient resources, training) when supporting maternal postpartum bonding, they act as the bridge between mothers and infants, becoming advocates for NICU families and taking care of their growth and developmental needs as caregivers.

Development and use of an adjusted nurse staffing metric in the neonatal intensive care unit.

OBJECTIVE: To develop a nurse staffing prediction model and evaluate deviation from predicted nurse staffing as a contributor to patient outcomes. DATA SOURCES: Secondary data collection conducted 2017-2018, using the California Office of Statewide Health Planning and Development and the California Perinatal Quality Care Collaborative databases. We included 276 054 infants born 2008-2016 and cared for in 99 California neonatal intensive care units (NICUs). STUDY DESIGN: Repeated-measures observational study. We developed a nurse staffing prediction model using machine learning and hierarchical linear regression and then quantified deviation from predicted nurse staffing in relation to health care-associated infections, length of stay, and mortality using hierarchical logistic and linear regression. DATA COLLECTION METHODS: We linked NICU-level nurse staffing and organizational data to patient-level risk factors and outcomes using unique identifiers for NICUs and patients. PRINCIPAL FINDINGS: An 11-factor prediction model explained 35 percent of the nurse staffing variation among NICUs. Higher-than-predicted nurse staffing was associated with decreased risk-adjusted odds of health care-associated infection (OR: 0.79, 95% CI: 0.63-0.98), but not with length of stay or mortality. CONCLUSIONS: Organizational and patient factors explain much of the variation in nurse staffing. Higher-than-predicted nurse staffing was associated with fewer infections. Prospective studies are needed to determine causality and to quantify the impact of staffing reforms on health outcomes.