Key factors to facilitate locally driven family planning programming: a qualitative analysis of urban stakeholder perspectives in Africa and Asia

Globalization and Health(2021)

引用 4|浏览0
暂无评分
摘要
Background There has been greater recognition of the importance of country ownership in global health and development. However, operationalising country ownership to ensure the scale up and sustainability of proven interventions remains elusive at best. To address this challenge, we undertook a thematic analysis of interviews collected from representatives of local governments, public health systems, and communities in poor urban areas of East Africa, Francophone West Africa, India, and Nigeria, supported by The Challenge Initiative (TCI), aiming to rapidly and sustainably scale up evidence-based reproductive health and family planning solutions. Methods The main objective of this study was to explore critical elements needed for implementing and scaling evidence-based family planning interventions. The research team conducted thematic analysis of 96 stories collected using the Most Significant Change (MSC) technique between July 2018 and September 2019. After generating 55 unique codes, the codes were grouped into related themes, using TCI’s model as a general analytical framework. Results Five key themes emerged: (1) strengthening local capacity and improving broader health systems, (2) shifting mindsets of government and community toward local ownership, (3) institutionalising the interventions within existing government structures, (4) improving data demand and use for better planning of health services, and (5) enhancing coordination of partners. Conclusion While some themes feature more prominently in a particular region than others, taken together they represent what stakeholders perceive to be essential elements for scaling up locally-driven health programmes in urban areas in Africa and Asia.
更多
查看译文
关键词
Family planning, Local government, Local ownership, Health system strengthening, Evidenced-based interventions, Scale-up
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要