Factors associated with childbirth readiness among pregnant women: a Bayesian network analysis

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. 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. 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 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%). Insufficient readiness for childbirth may not only exert a negative influence on the birthing experience of women, but may also diminish their willingness to conceive again. This study investigated 350 pregnant women in Wuxi, Jiangsu Province, China, and employed a Bayesian network model to construct the structural relationships among the factors influencing childbirth readiness. The aim was to identify the determinants of childbirth readiness and their interrelated pathways, thereby providing empirical evidence to support the improvement of childbirth readiness. The Bayesian model revealed that self-efficacy, fear of childbirth, electronic health literacy, and parity were the most strongly associated nodes with childbirth readiness, whereas planned pregnancy, antenatal exercise, family support, and maternal age were indirectly associated through other nodes. Specifically, women with previous childbirth experience, high levels of confidence in their own abilities, proficient use of online health information, and minimal fear of childbirth exhibited the highest levels of childbirth readiness. These findings indicate that health care professionals should pay particular attention to three groups: primiparas, women with low self-confidence, and women who encounter difficulties in accessing reliable health information. By providing antenatal education, developing personalised exercise programmes, offering digital health support, and encouraging family involvement, it is possible to enhance maternal readiness for childbirth and foster a more positive birthing experience.