Background: Sexual satisfaction is a desired feeling that one experiences during sexual interaction and is affected by several factors. Objectives: This study was conducted to examine the sexual satisfaction and its correlates among married women. Patients and Methods: This cross-sectional study comprised 306 married women in their reproductive age, selected by convenience sampling method, and referred to the four health centers affiliated to Tehran University of Medical Sciences in 2012, the participants completed a researcher-made questionnaire. Statistical analysis was carried out using independent samples T-test, Chi-square and logistic regression through SPSS version 16. Results: The mean score of the sexual satisfaction was 77.97 ± 1.38. Based on the mean, women were divided into two groups: sexually satisfied and dissatisfied. The Two groups matched in terms of age (P = 0.35), age of the husbands (P= 0.26), income status (P = 0.43), number of children (P = 0.44) and contraceptive methods (P = 0.13). Participants' educational level, menstrual status, marital duration, sexual function, husband's educational status and emotional bound were entered into the logistic regression model. Emotional bound had a significant effect on sexual satisfaction (P = 0.04, OR = 1.54, CI = 1.01 -2.36). Conclusions: Emotional bound as a considerable trait is associated with women's sexual satisfaction. It is recommended that health care providers pay more attention to this point at the time of health care delivery and also to emphasize the renovation of interpersonal relationship.

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Background Growing the worldwide and Iranian cesarean section rate and rising morbidity and mortality thereafter for the mother and infant has been an important health issue. Predictive models can identify individuals with a higher probability of cesarean section and make better decisions. In this study, we investigated the bio-psychosocial factors associated with type of delivery. We designed a predictive model using the decision tree C4.5 algorithm. Methods In this longitudinal study 170 pregnant women were sampled in the third trimester of pregnancy. At the baseline phase blood samples were taken from mothers to measure estrogen hormone. Birth information was recorded at the follow-up time at 30–42 days postpartum. Modeling was performed using MATLAB software and C4.5 decision tree algorithm using input variables and the target variable. Results Previous type of childbirth, maternal body mass index at childbirth, maternal age, and serum estrogen were the most significant factors in predicting the childbirth type, respectively and decision tree model with 89.6%accuracy in the training stage and 83.3% in the test stage predicted the result. Conclusion The decision tree model designed with high accuracy and sensitivity can predict the type of childbirth. By recognizing the contributing factors and model rules, health practitioners and policymakers can take preventive action.