摘要
目的：对产妇行责任制助产护理，观察其分娩方式以及母婴结局。方法：择取本院于2015年1月-2016年6月期间收治的产妇90例，按照随机双盲法，将产妇划分为观察组与参照组，每组各45例。对参照组产妇行传统分娩护理，对观察组产妇行责任助产方式分娩，对比两组产妇的分娩方式以及母婴结局。结果：在分娩方式方面，观察组产妇的自然分娩率为91.11%，剖腹产率为8.89%；参照组产妇的自然分娩率为66.67%，剖腹产率为33.33%，对比两组数据，组间差异统计学意义具有明显性，即P<0.05；在母婴结局方面，观察组产妇的产后出血量、下肢疼痛麻木率、胎儿窘迫率以及新生儿窒息率与参照组产妇进行对比，相应数据组间优势显著，即P<0.05，存在明显的统计学意义。结论：产妇临床分娩过程中，对其行责任制助产护理，不仅可以提高产妇自然分娩率，还能促使母婴结局实现良性发展，对于保证母婴生命安全与健康，具有非常重要的现实意义，值得临床实践普及应用。
Abstract
Objective: maternal line of accountability obstetric care to observe the mode of delivery and maternal and neonatal outcomes. Method: choose to take the hospital in January 2015 - June 2016 were treated 90 cases of maternal period, according to randomized double-blind, maternal divided into observation group and the reference group, 45 cases in each group. Maternal line of the reference group of traditional delivery care, maternal-line responsibility for the observation group midwifery mode of delivery, mode of delivery compared two groups of mothers and maternal outcome. Results: In terms of mode of delivery, maternal natural childbirth observation group was 91.11%, caesarean section rate was 8.89%; reference group maternal natural birth rate of 66.67%, 33.33% rate of caesarean section, compared to two sets of data, statistical differences between groups has obvious significance, namely P <0.05; in terms of maternal and neonatal outcomes, maternal postpartum hemorrhage observation group, the rate of lower extremity pain and numbness, fetal distress and neonatal asphyxia rate and reference groups were compared between data sets corresponding advantages significantly, namely P <0.05, significant statistical significance. Conclusion: The clinical course of women in childbirth, midwifery care for its line of accountability, not only can improve maternal natural birth rate, but also to promote maternal and child outcomes to achieve healthy development, to ensure the safety and health of mother and child, has a very important practical significance worthy of clinical practice universal application.
NETL
NSTL
维普
万方数据