Influencing factors for childbirth readiness among pregnant women based on the reciprocal determinism theory and backpropagation neural network: a cross-sectional study in China.

BACKGROUND

Childbirth readiness is essential for improving maternal health outcomes and reducing mortality, yet preparedness remains low among pregnant women globally. This study aims to identify key factors influencing childbirth readiness among Chinese women, using reciprocal determinism theory and a backpropagation neural network.

METHODS

A cross-sectional study was conducted with 816 pregnant women from three hospitals in Hubei, China. A structured questionnaire based on reciprocal determinism theory was designed to collect data on individual and environmental factors. Backpropagation neural network modeling and multiple linear regression were used to identify and rank the importance of the factors.

RESULTS

Backpropagation neural network analysis revealed that behavioral intention in childbirth preparation, family care, information support, and fear of birth were the strong predictors of childbirth readiness, with behavioral intention in childbirth preparation being the most significant. Additional influential factors included travel time to the nearest hospital and satisfaction with antenatal care. The backpropagation neural network showed good predictive accuracy.

CONCLUSION

This study innovatively used reciprocal determinism and backpropagation neural network modeling to identify key childbirth readiness factors, highlighting behavioral intention in childbirth preparation, family care, fear of birth, and information support. Integrating intrinsic motivation with extrinsic support enhances maternal readiness and provides strategies for improved antenatal care.