Development of a surveillance and monitoring model for pregnant women with preterm labor

Abstract

Preterm labor is a cause of premature birth, resulting in infant death. This research and development aimed to study the problem, develop a model, and evaluate the model. The sample was purposively selected, consisting of 30 pregnant women with preterm labor, and 13 people participated in the model development. The tools consisted of 2 parts: research tools including focus group discussions, surveillance and monitoring models, and data collection tools including a knowledge questionnaire on preterm labor, a preterm birth prevention behavior scale, satisfaction questionnaire, and a model evaluation form. Data were analyzed using descriptive statistics and inferential statistics.

The study results found that the problem of premature birth was caused by recurrent preterm labor pains. Pregnant women lacked knowledge about self-care at home, lacked channels to contact officials, and lacked close follow-up. Surveillance and monitoring methods included discharge planning according to the D-METHOD principles, providing knowledge using Brain Based Learning, monitoring via Line OA, and making follow-up appointments. The results showed that readmissions decreased, full-term births increased, and knowledge and preventative behaviors significantly improved with a p-value of <0.001. Pregnant women and the interdisciplinary team were highly satisfied.

Recommendations: The research results indicate that the developed model has the potential to reduce preterm birth. It should be adapted for use in community hospitals and applied to other at-risk pregnant women to confirm the comprehensiveness of the model.