Purpose: Maternal mental health disorders are prevalent among migrant women. Due to the association of these disorders with adverse pregnancy outcomes, early recognition, and referral are important. This review aims to provide an overview of the literature on mental health screening for migrant women during pregnancy and the postpartum period.
View Article and Find Full Text PDFBackground: Forcibly displaced women in the Netherlands face increased chances of perinatal mortality and other adverse pregnancy and childbirth outcomes compared to the resident country population, which has been linked to suboptimal care. This study was conducted to gain insights from the experiences of Dutch midwives to inform and enhance the provision of tailored and equitable care for forcibly displaced women.
Methods: We conducted a qualitative study using semistructured interviews with community midwives who provide care for forcibly displaced women (asylum seekers and recognized refugees) in the Netherlands.
Introduction: Refugees and their healthcare providers face numerous challenges in receiving and providing maternal and newborn care. Research exploring how these challenges are related to adverse perinatal and maternal outcomes is scarce. Therefore, this study aims to identify suboptimal factors in maternal and newborn care for asylum-seeking and refugee women and assess to what extent these factors may contribute to adverse pregnancy outcomes in the Netherlands.
View Article and Find Full Text PDFBackground: The rise of forced migration worldwide compels birth care systems and professionals to respond to the needs of women giving birth in these vulnerable situations. However, little is known about the perspective of midwifery professionals on providing perinatal care for forcibly displaced women. This study aimed to identify challenges and target areas for improvement of community midwifery care for asylum seekers (AS) and refugees with a residence permit (RRP) in the Netherlands.
View Article and Find Full Text PDFObjective: Distinguishing normal, neuropathic and myopathic electromyography (EMG) traces can be challenging. We aimed to create an automated time series classification algorithm.
Methods: EMGs of healthy controls (HC, n = 25), patients with amyotrophic lateral sclerosis (ALS, n = 20) and inclusion body myositis (IBM, n = 20), were retrospectively selected based on longitudinal clinical follow-up data (ALS and HC) or muscle biopsy (IBM).