How capacity building of district health managers has been designed, delivered and evaluated in sub-Saharan Africa: a scoping review and best fit framework analysis

Abstract

ObjectivesWe aimed to understand how capacity building programmes (CBPs) of district health managers (DHMs) have been designed, delivered and evaluated in sub-Saharan Africa. We focused on identifying the underlying assumptions behind leadership and management CBPs at the district level.DesignScoping review.Data sourcesWe searched five electronic databases (MEDLINE, Health Systems Evidence, Wiley Online Library, Cochrane Library and Google Scholar) on 6 April 2021 and 13 October 2022. We also searched for grey literature and used citation tracking.Eligibility criteriaWe included all primary studies (1) reporting leadership or management capacity building of DHMs, (2) in sub-Saharan Africa, (3) written in English or French and (4) published between 1 January 1987 and 13 October 2022.Data extraction and synthesisThree independent reviewers extracted data from included articles. We used the best fit framework synthesis approach to identify an a priori framework that guided data coding, analysis and synthesis. We also conducted an inductive analysis of data that could not be coded against the a priori framework.ResultsWe identified 2523 papers and ultimately included 44 papers after screening and assessment for eligibility. Key findings included (1) a scarcity of explicit theories underlying CBPs, (2) a diversity of learning approaches with increasing use of the action learning approach, (3) a diversity of content with a focus on management rather than leadership functions and (4) a diversity of evaluation methods with limited use of theory-driven designs to evaluate leadership and management capacity building interventions.ConclusionThis review highlights the need for explicit and well-articulated programme theories for leadership and management development interventions and the need for strengthening their evaluation using theory-driven designs that fit the complexity of health systems.