The Effect of Training on Knowledge and Skill of Early Detection of High Risk Pregnancy in Community Health Workers in Semarang, Central Java


Kralita Intan Mulya Hapsari1), Dodik Pramono2), Aras Utami2)

1)Faculty of Medicine, Universitas Diponegoro, Semarang

2)Department of Public Health, Faculty of Medicine,

Universitas Diponegoro, Semarang

 

ABSTRACT

Background: The maternal mortality ratio (MMR) in Indonesia is still high with an estimated 305 maternal deaths per 100,000 live births. Community health workers (CHWs) play a vital role in facilitating the continuum of care by acting as the bridge between the community and the health facility. However, CHWs are often not well-trained and many do not have the instruments needed to early detect high risk pregnancy. This study aimed to evaluate the effectiveness of training in improving knowledge and skill of CHWs in early detection of high risk pregnancy.

Subjects and Method: This was a quasi-experimental study with one group pre and post test design. The study was conducted in Bandarharjo, Semarang, Central Java, from July to August 2017. A sample of 39 community health workers (CHWs) was selected for this study. The CHWs received training on early detection of high-risk pregnancy. The dependent variables were knowledge and skill in the early detection of high risk pregnancy. The independent variable was training. The data were collected by questionnaire and analyzed by Wilcoxon test.

Results: Knowledge after training (mean= 81.73, SD= 15.10) was higher than before training (mean= 76.28; SD= 13.58) with p= 0.006. Skill after training (mean= 79.13, SD= 9.41) was higher before training (mean= 58.84; SD= 10.08) with p<0.001.

Conclusion: Training is effective in improving knowledge and skill in the early detection of high risk pregnancy of the community health workers.

Keywords: training, early detection, high risk pregnancy, knowledge, skill, community health worker

Correspondence: Aras Utami. Faculty of Medicine, Universitas Diponegoro, Semarang, Central Java. Email: aras.utami@gmail.com. Mobile: 081225273747.

DOI: https://doi.org/10.26911/mid.icph.2018.03.33

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