Evaluating a model for the capacity building of midwifery eduators in Bangladesh through a blended, web-based...

Background: While setting international standards for midwifery education has attracted considerable global attention, the education and training of midwifery educators has been relatively neglected, particularly in low-resource settings where capacity building is crucial. Objective: The aim of this study was to describe the expectations of midwifery educators in Bangladesh who took part in a blended web-based master's programme in SRHR and the extent to which these were realized after 12 months of part-time study. Methods: Both quantitative and qualitative methods have been used to collect data. A structured baseline questionnaire was distributed to all participants at the start of the first course (n = 30) and a second endpoint questionnaire was distributed after they (n = 29) had completed the core courses one year later. At the start of the first course, five focus group discussions (FGD) were held with the midwifery educators. Descriptive statistics and content analysis were used for the analyses. Results: Midwifery educators who took part in the study identified expectations that can be grouped into three distinct areas. They hoped to become more familiar with technology, anticipated they would learn pedagogical and other skills that would enable them to better support their students' learning and thought they might acquire skills to empower their students as human beings. Participants reported they realized these ambitions, attributing the master's programme with helping them take responsibility for their own teaching and learning, showing them how to enhance their students' learning and how to foster reflective and critical thinking among them. Conclusions: Midwifery educators have taken part in a creative learning environment which has developed their engagement in teaching and learning. They have done this using a blended learning model which combines online learning with face-to-face contact. This model can be scaled up in low resource and remote settings.