2019年在埃塞俄比亚南沃罗滕塔沃雷达进行的一项基于设施的横断面研究中,在卫生机构接受产前保健的孕妇对产科危险体征和相关因素的了解

Workinesh Sinashaw Ababe
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引用次数: 1

摘要

背景:产妇发病和死亡相关的可预防原因中,延误是主要原因之一,知识贫乏被认为是促成因素之一。本研究的目的是评估南沃罗州卫生机构Tenta Woreda的产前母亲对妊娠危险体征及其相关因素的知识水平。方法:本研究的目的是评估孕妇对产科危险体征及其相关因素的知识水平。采用以机构为基础的横断面研究方法,将全国所有卫生机构纳入研究,样本量采用单人口比例计算,在前人研究(Yirga cheefe)的基础上,样本量为21.9%,即293名妇女,按比例分配到各自的卫生机构,采用系统抽样的方法获得每个样本个体。数据收集工具分为社会人口部分、生殖部分和基于知识的三部分。资料采用访谈法收集。使用epi数据(4.2版本)输入数据,导出到SPSS 25.00版本进行清理和分析,并采用Logistic回归分析来确定与产科危险体征知识相关的因素。使用95% CI将p值<0.05的变量确定为具有统计学意义的因素。
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Knowledge of Obstetric Danger Signs and Associated Factors among Pregnant Women Attending Antenatal Care at Health Facilities, in Tenta Woreda, South Wollo, Ethiopia 2019, A Facility Based Cross Sectional Study
Background: Maternal morbidity and mortality related preventable cause among this the three delay are the main one, poor knowledge is believed one the contributing factor. The purpose of this study was to assess the knowledge danger signs of pregnancy and associated factors of antenatal mother in Tenta Woreda, Health Facilities, of South Wollo, Methods: The aim of this study was to assess the level of knowledge of pregnant mother and factors associated about obstetric danger signs. Institution based cross-sectional study was employed All the health facility in the woreda were included in the study and , the sample size calculated using single population proportion with the proportion of 21.9% based on previous study (Yirga chefe) it was 293 women’s ,the study subject proportionally allocated to the respective facility and systematic sampling was used to get every sample individual. The data collection tool had three parts sociodemographic part, reproductive part and knowledge based. The data were collected by interviewed method. The data was entered using epi data (version 4.2) and export to SPSS version 25.00 for clearing and analysis and Logistic regression analyses were employed to identify factors associated with knowledge of obstetric danger signs. Using 95% CI variables with a p-value <0.05 were identified as statistically significant factors.
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