Peer-reviewed article
Factors associated with childbirth readiness among pregnant women: a Bayesian network analysis.
Ningying Zhou, Feng Zhang, Min Liu, Shanshan Hu, Yuqing Zan et al. ·
Summary
Written by AI, not yet AICS's view · claude-haiku-4-5-20251001This study examined factors that help pregnant women feel ready for childbirth among 350 women in China. Researchers identified three levels of childbirth readiness and found that a woman's confidence in herself, fear of childbirth, ability to understand health information online, and whether she had given birth before were the strongest predictors. Women with high confidence, low fear, good digital health literacy, and prior birth experience had the best readiness for childbirth.
Key points
- Childbirth readiness was categorised into three levels: poor (26%), good (30.9%), and complete (43.1%)
- Self-efficacy, fear of childbirth, eHealth literacy, and parity were the most direct factors associated with childbirth readiness
- Women with high self-efficacy, no fear of childbirth, high eHealth literacy, and multiparity had 83.3% probability of complete childbirth readiness
- Planned pregnancy, antenatal exercise, family support, and age were linked indirectly through other factors
- Previous regression models could not explain the interconnections among influencing factors; Bayesian network analysis revealed these relationships
Limitations
This was a cross-sectional study at a single hospital in China, so findings may not generalise to other populations or healthcare settings. The study identifies associations but cannot establish causation.
Abstract
BACKGROUND: Inadequate childbirth readiness can adversely affect the birthing experience of pregnant women and may even influence their willingness to have further children. This study aimed to explore the determinants of childbirth readiness and the network relationships among these factors, thereby providing evidence to improve childbirth readiness. METHODS: This cross-sectional study surveyed 350 pregnant women attending Wuxi Maternity and Child Health Care Hospital. Latent profile analysis (LPA) was first performed using the four domains of the Childbirth Readiness Scale to identify subgroups of childbirth readiness, and potential associated factors were then screened using univariate analysis and multinomial logistic regression. A Bayesian network model was employed to construct the structural relationships of factors influencing childbirth readiness. RESULTS: Childbirth readiness was categorised into three levels: poor (26%), good (30.9%), and complete (43.1%). Univariate analysis revealed significant differences across the three categories in relation to age, parity, pregnancy complications, antenatal exercise, planned pregnancy, self-efficacy, eHealth literacy, fear of childbirth, and family support (p < 0.2). Multinomial logistic regression indicated that parity, self-efficacy, and eHealth literacy were important predictors of childbirth readiness. The Bayesian model identified self-efficacy, fear of childbirth, eHealth literacy, and parity as the nodes most closely associated with childbirth readiness, while planned pregnancy, antenatal exercise, family support, and age were linked indirectly through other nodes. CONCLUSIONS: Previous studies on childbirth readiness have mainly relied on regression models, which are unable to elucidate the intrinsic interconnections among influencing factors. By constructing a Bayesian model, this study demonstrated that women with high self-efficacy, no fear of childbirth, high eHealth literacy, and multiparity had the highest probability of achieving complete childbirth readiness (83.3%).