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Role of Technology in Maternal Health Education to Improve Childbirth Readiness. 3 https://doi.org/10.58209/hehp.13.2.323
URL: http://daneshafarand.org/article-1-80098-en.html
URL: http://daneshafarand.org/article-1-80098-en.html
Abstract: (1554 Views)
Aims: This study aimed to analyze the role of technology in maternal health education to improve maternal readiness, measure its effectiveness in terms of knowledge, mental and physical readiness, and identify challenges and implementation strategies.
Instrument & Methods: This quantitative research utilized partial least squares structural equation modeling with a sample of 99 pregnant women at the Umar Damanik Health Center in Tanjung Balai. Sampling was conducted using the Slovin formula (with a 5% margin of error). Data were collected through valid and reliable questionnaires and analyzed using SmartPLS to test construct reliability (Cronbach’s alpha, composite reliability), convergent validity (average variance extracted), and hypothesis testing (bootstrapping).
Findings: All constructs met the reliability and validity criteria (Cronbach’s alpha>0.7; AVE>0.5). Technology significantly influenced childbirth readiness (R²=0.717-0.899; p<0.05). Mobile health applications provided interactive information access, IoT facilitated real-time monitoring, streaming platforms expanded educational resources, and telemedicine increased access to health consultations. The PLS-SEM model explained 84.1% of the data variability (adjusted R²).
Conclusion: Technology effectively improves childbirth readiness through accessible, interactive, and personalized education.
Instrument & Methods: This quantitative research utilized partial least squares structural equation modeling with a sample of 99 pregnant women at the Umar Damanik Health Center in Tanjung Balai. Sampling was conducted using the Slovin formula (with a 5% margin of error). Data were collected through valid and reliable questionnaires and analyzed using SmartPLS to test construct reliability (Cronbach’s alpha, composite reliability), convergent validity (average variance extracted), and hypothesis testing (bootstrapping).
Findings: All constructs met the reliability and validity criteria (Cronbach’s alpha>0.7; AVE>0.5). Technology significantly influenced childbirth readiness (R²=0.717-0.899; p<0.05). Mobile health applications provided interactive information access, IoT facilitated real-time monitoring, streaming platforms expanded educational resources, and telemedicine increased access to health consultations. The PLS-SEM model explained 84.1% of the data variability (adjusted R²).
Conclusion: Technology effectively improves childbirth readiness through accessible, interactive, and personalized education.
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