ABSTRACT
Background: Although ante natal care and institutional delivery is effective means for reducing maternal morbidity and mortality; the probability of giving birth at health institutions among ante natal care attendants has not been modeled in Ethiopia. Therefore; the objective of this study was to model predictors of giving birth at health institutions among expectant mothers following antenatal care. Methods: Facility based cross sectional study design was conducted among 322 consecutively selected mothers who were following ante natal care in two districts of West Shewa Zone; Oromia Regional State; Ethiopia. Participants were proportionally recruited from six health institutions. The data were analyzed using SPSS version 17.0. Multivariable logistic regression was employed to develop the prediction model. Results: The final regression model had good discrimination power (89.2); optimum sensitivity (89.0) and specificity (80.0) to predict the probability of giving birth at health institutions. Accordingly; self efficacy (beta=0.41); perceived barrier (beta=-0.31) and perceived susceptibility (beta=0.29) were significantly predicted the probability of giving birth at health institutions. Conclusion: The present study showed that logistic regression model has predicted the probability of giving birth at health institutions and identified significant predictors which health care providers should take into account in promotion of institutional delivery
Subject(s)
Parturition , Pregnant Women , Prenatal Education , ProbabilityABSTRACT
Background: Patient enablement is associated with behaviours like treatment adherence and self-care and is becoming a well-accepted indicator of quality of care. However, the concept of patient enablement has never been subjected to scientific inquiry in Ethiopia. Objectives: The aim of this study was to determine the degree of patient enablement and its predictors after consultation at primary health care centres in central Ethiopia. Method: Data were collected from 768 outpatients from six primary health care centres in central Ethiopia during a cross-sectional study designed to assess patient satisfaction. Consecutive patients, 15 years or older, were selected for the study from each health centre. Multinomial logistic regression was performed to identify predictors of patient enablement using SPSS (version 16.0). Results: The study showed that 48.4% of patients expressed an intermediate level of enablement, while 25.4% and 26.2% of the patients expressed low and high levels of patient enablement, respectively. Four models were developed to identify predictors of patient enablement. The first model included socio-demographic variables, showing that residence, educational status and occupational status were significantly associated with patient enablement (p < 0.05). This model explained only 20.5% of the variation. The second and third models included institutional aspects, and perceived doctorpatient interaction and information sharing about illness, respectively. They explained 31.1% and 64.9% of the variation. The fourth model included variables that were significantly associated with patient enablement in the first, second and third models and explained 72% of the variation. In this model, perceived empathy and technical competency, non-verbal communication, familiarity with the provider, information sharing about illness and arrangement for follow-up visits were strong predictors of patient enablement (p <0.05). Conclusion: The present study revealed specific predictors of patient enablement, which health care providers should consider in their practice to enhance patient enablement after consultation