Evidence map›Paper›PMID 41088076›Full record

ArticleBMC pregnancy and childbirth2025

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

Yuan Mengmei, Zhao Meizhen, Zeng Tieying, Wu Meiliyang, Zhang Ke

Abstract read
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Article in BMC pregnancy and childbirth, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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3 · Its place in the literature

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2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Yuan Mengmei *Department of Nursing, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Road, Wuhan, 430030, China.
Zhao Meizhen *Department of Nursing, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Road, Wuhan, 430030, China.
Zeng TieyingDepartment of Nursing, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Road, Wuhan, 430030, China. 984451641@qq.com.
Wu MeiliyangDepartment of Nursing, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Road, Wuhan, 430030, China.
Zhang KeDepartment of Nursing, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Road, Wuhan, 430030, China.

Funding

National Natural Science Foundation of China No. 72304105
6 · The paper itself

Abstract

backgroundChildbirth 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.

methodsA 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.

resultsBackpropagation 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.

conclusionThis 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.

Indexed as

Neural Networks, ComputerParturitionPregnant PeoplePrenatal CareAdultChinaCross-Sectional StudiesDelivery, ObstetricFearFemaleHealth Knowledge, Attitudes, PracticeHumansIntentionMotivationPregnancySurveys and QuestionnairesChildbirth readinessCross-Sectional studiesMaternal health servicesNeural networksPregnant womenPrenatal care

Identifiers

PMID41088076
PMCPMC12522223

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.