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An innovative web-based decision-aid about birth after cesarean for shared decision making in Taiwan: study protocol
Shu Wen Chen1, Allison Shorten2, Chang Ching Yeh3
1School of Nursing, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan. shuwen@ntunhs.edu.tw.
Background:
Taiwan has a high national caesarean rate coupled with a low vaginal birth after caesarean (VBAC) rate. This study aims to develop and evaluate a web-based decision-aid with communication support tools, to increase shared decision making (SDM) about birth after caesarean.
Methods:
A quantitative approach will be adopted using a randomized pre-test and post-test experimental design in a medical centre in northern Taiwan. The web-based decision aid consists of five sections. Section 1 provides a two-part video to introduce SDM and how to participate in SDM. Section 2 presents an overview of functions and features of the birth decision-aid. Section 3 presents relevant VBAC information, including definitions, benefits and risks, and an artificial intelligence (AI) calculator for rate and likelihood of VBAC success. Section 4 presents the information regarding elective repeat caesarean delivery (ERCD), involving definitions, benefits, and risks. Section 5 comprises four steps of decision making to meet women's values and preferences. Pregnant women who have had one previous caesarean and are eligible for VBAC, will be recruited at 14-16 weeks. Participants will complete a baseline survey prior to random allocation to either the control group (usual care) or intervention group (usual care plus an AI-decision aid). A follow up survey at 35-38 weeks will measure change in decisional conflict, knowledge, birth mode preference, and decision-aid acceptability. Actual birth outcomes and satisfaction will be assessed one month after birth.
Discussion:
The innovative web-based decision-aid with support tools will help to promote pregnant women's decision-making engagement and communication with their providers and improve opportunities for supportive communication about VBAC SDM in Taiwan. Linking web-based AI data analysis into the medical record will also be assessed for feasibility during implementation in clinical practice.
Trial Registration:
ClinicalTrials.gov identifier (NCT05091944), Registered on October 24, 2021.
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