A Community Health Worker “logic model”: towards a theory of enhanced performance in low- and middle-income countries

BACKGROUND There has been a resurgence of interest in national Community Health Worker (CHW) programs in low- and middle-income countries (LMICs). A lack of strong research evidence persists, however, about the most efficient and effective strategies to ensure optimal, sustained performance of CHWs at scale. To facilitate learning and research to address this knowledge gap, the authors developed a generic CHW logic model that proposes a theoretical causal pathway to improved performance. The logic model draws upon available research and expert knowledge on CHWs in LMICs. METHODS Construction of the model entailed a multi-stage, inductive, two-year process. It began with the planning and implementation of a structured review of the existing research on community and health system support for enhanced CHW performance. It continued with a facilitated discussion of review findings with experts during a two-day consultation. The process culminated with the authors' review of consultation-generated documentation, additional analysis, and production of multiple iterations of the model. RESULTS The generic CHW logic model posits that optimal CHW performance is a function of high quality CHW programming, which is reinforced, sustained, and brought to scale by robust, high-performing health and community systems, both of which mobilize inputs and put in place processes needed to fully achieve performance objectives. Multiple contextual factors can influence CHW programming, system functioning, and CHW performance. CONCLUSIONS The model is a novel contribution to current thinking about CHWs. It places CHW performance at the center of the discussion about CHW programming, recognizes the strengths and limitations of discrete, targeted programs, and is comprehensive, reflecting the current state of both scientific and tacit knowledge about support for improving CHW performance. The model is also a practical tool that offers guidance for continuous learning about what works. Despite the model's limitations and several challenges in translating the potential for learning into tangible learning, the CHW generic logic model provides a solid basis for exploring and testing a causal pathway to improved performance.