A guide to systems-level, participatory, theory-informed implementation research in global health

Abstract

Implementation research is a multidisciplinary field that addresses the complex phenomenon of how context influences our ability to deliver evidence-informed healthcare. There is increasing realisation of the importance of applying robust implementation research to scale-up lifesaving interventions that meet health-related sustainable development goals. However, the lack of high-quality implementation research is impeding our ability to meet these targets. In this paper we provide guidance to assist researchers to understand and conceptualise how implementation research methods can be used to deliver high-quality evidence-informed practice at scale. There are four phases to implementation research: pre-implementation, piloting, implementation and evaluation, and post-implementation and scale-up phases. Implementation science and theory-driven approaches can be used to design, pilot, implement and evaluate implementation programmes. Important components of implementation research such as stakeholder engagement can be applied to embed the research into existing health systems therefore ensure applicability in the ‘real world’. Implementation research also needs to be participatory; co-designed by all stakeholders who can influence implementation efforts. Although there is increasing interest in applying robust methodology to understand how and why implementation programmes work in real-world settings, global health actors still tend to favour evaluations conducted in controlled environments. Perhaps this is due to implementation research being a multidisciplinary field requiring expertise from a range of specialist disciplines such as implementation science and social science. To help translate some of the ambiguity surrounding the methodologies applied to implementation research, we draw on our expertise from a range of disciplines including global health, social science, policy, implementation science, epidemiology, and statistics to offer an overview of how to conduct participatory, theory-driven implementation research to design and test the effectiveness of delivering high-quality evidence-informed interventions at scale.

Understanding how a digital mental health intervention can be optimised to ensure effectiveness in the longer-term: findings from a causal mediation analyses of the CONEMO trials

“…The future of mHealth applications, facilitated by the gradual transition toward remote care in recent years arising during the COVID-19 pandemic, shows great promise to reduce existing large treatment gaps in mental health, especially with the rapid development of robust methodologies to understand how to adapt intervention to improve outcomes, including causal machine learning(Benkeser and Ran, 2021) and robust implementation research. (Seward et al ., 2021)…”

Context and priorities for health systems strengthening for pain and disability in low- and middle-income countries: a secondary qualitative study and content analysis of health policies

“…Here, consultation with local health systems experts and people with lived experience, in tandem with examination of the contemporary policy landscape in LMICs, are important considerations for HSS solutions ( Heller et al. , 2019 ; Seward et al. , 2021 ).…”