Evaluating patient experience in maternity services using a Bayesian belief network model

Pregnancy and childbirth are commonly seen as positive experiences, but they can also pose distinct challenges and risks, especially when care is insufficient. This study investigates the factors influencing maternity patient experience by exploring the complex interactions among these factors. Usin...

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Veröffentlicht in:	PloS one Jg. 20; H. 2; S. e0318612
Hauptverfasser:	Munassar, Abrar Abdulhakim Ahmed, Simsekler, Mecit Can Emre, Saad, Ahmed Alaaeldin, Qazi, Abroon, Omar, Mohammed A.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Public Library of Science 20.02.2025 Public Library of Science (PLoS)
Schlagworte:	Adult Analysis Bayes Theorem Bayesian analysis Bayesian statistical decision theory Belief networks Biology and Life Sciences Childbirth & labor Clinical decision making Computer and Information Sciences Decision trees England Evaluation Female Health surveys Humans Maternal child nursing Maternal health services Maternal Health Services - standards Maternal mortality Mathematical models Medical personnel Medicine and Health Sciences Missing data Patient satisfaction Patient Satisfaction - statistics & numerical data Patients Physical Sciences Pregnancy Prenatal Care Random variables Research and Analysis Methods Sensitivity analysis Social Sciences Surveys Surveys and Questionnaires Womens health England United Arab Emirates
ISSN:	1932-6203, 1932-6203
Online-Zugang:	Volltext
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