Published in:
Open Access 01-12-2013 | Study protocol
Study protocol for promoting respectful maternity care initiative to assess, measure and design interventions to reduce disrespect and abuse during childbirth in Kenya
Published in: BMC Pregnancy and Childbirth | Issue 1/2013
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Background
Increases in the proportion of facility-based deliveries have been marginal in many low-income countries in the African region. Preliminary clinical and anthropological evidence suggests that one major factor inhibiting pregnant women from delivering at facility is disrespectful and abusive treatment by health care providers in maternity units. Despite acknowledgement of this behavior by policy makers, program staff, civil society groups and community members, the problem appears to be widespread but prevalence is not well documented. Formative research will be undertaken to test the reliability and validity of a disrespect and abuse (D&A) construct and to then measure the prevalence of disrespect and abuse suffered by clinic clients and the general population.
Methods/design
A quasi-experimental design will be followed with surveys at twelve health facilities in four districts and one large maternity hospital in Nairobi and areas before and after the introduction of disrespect and abuse (D&A) interventions. The design is aimed to control for potential time dependent confounding on observed factors.
Discussion
This study seeks to conduct implementation research aimed at designing, testing, and evaluating an approach to significantly reduce disrespectful and abusive (D&A) care of women during labor and delivery in facilities. Specifically the proposed study aims to: (i) determine the manifestations, types and prevalence of D&A in childbirth (ii) develop and validate tools for assessing D&A (iii) identify and explore the potential drivers of D&A (iv) design, implement, monitor and evaluate the impact of one or more interventions to reduce D&A and (v) document and assess the dynamics of implementing interventions to reduce D&A and generate lessons for replication at scale.