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 < 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.
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.

分娩准备不足不仅会对孕产妇的分娩体验产生负面影响，甚至会影响其再次生育意愿。本研究旨在探究分娩准备的影响因素及其内在网络关联，为提升分娩准备水平提供实证依据。
本项横断面研究纳入了无锡市妇幼保健院的350名孕产妇作为调查对象。研究首先采用分娩准备量表（Childbirth Readiness Scale）的四个维度开展潜在剖面分析（Latent Profile Analysis, LPA），以划分分娩准备水平的亚组；随后通过单因素分析与多项逻辑回归筛选潜在关联因素，并构建贝叶斯网络模型（Bayesian network model）以阐明分娩准备影响因素间的结构关联。
分娩准备水平被划分为三个等级：不足型（26%）、良好型（30.9%）与完备型（43.1%）。单因素分析结果显示，年龄、产次、妊娠并发症、产前运动、计划妊娠、自我效能感、电子健康素养（eHealth literacy）、分娩恐惧与家庭支持在三类分娩准备亚组间均存在显著差异（p<0.2）。多项逻辑回归分析表明，产次、自我效能感与电子健康素养是分娩准备水平的重要预测因子。贝叶斯网络模型显示，自我效能感、分娩恐惧、电子健康素养与产次是与分娩准备水平关联最为紧密的节点，而计划妊娠、产前运动、家庭支持与年龄则通过其他节点实现间接关联。
既往关于分娩准备的研究多依赖回归模型，难以阐明影响因素间的内在相互关联。本研究通过构建贝叶斯网络模型发现，自我效能感高、无分娩恐惧、电子健康素养水平高且经产妇的孕产妇，其分娩准备达到完备型的概率最高，达83.3%。
分娩准备不足不仅会对孕产妇的分娩体验造成负面影响，还会降低其再次妊娠的意愿。本研究针对中国江苏省无锡市的350名孕产妇开展调查，通过贝叶斯网络模型构建分娩准备影响因素的结构关联，旨在明确分娩准备的影响因素及其关联路径，为提升分娩准备水平提供实证依据。贝叶斯网络模型显示，自我效能感、分娩恐惧、电子健康素养与产次是与分娩准备水平关联最为紧密的节点，而计划妊娠、产前运动、家庭支持与孕产妇年龄则通过其他节点实现间接关联。具体而言，有分娩史、自我效能感高、能够熟练获取在线健康信息且分娩恐惧程度低的孕产妇，其分娩准备水平最高。本研究结果提示，医护人员应重点关注三类人群：初产妇、自我效能感低下者，以及难以获取可靠健康信息的人群。通过开展产前教育、制定个性化运动方案、提供数字化健康支持以及鼓励家庭参与，可有效提升孕产妇的分娩准备水平，营造更积极的分娩体验。