Balancing rigor and pragmatism in health research: a guide to choosing study designs for real-world settings

Abstract

BACKGROUND: Health research has traditionally privileged highly controlled study designs to maximise internal validity, yet such approaches often struggle to inform decision-making in complex, real-world settings such as health systems, public health programmes, and low- and middle-income countries. Increasingly, researchers and policy makers require evidence that is not only methodologically sound but also feasible, generalisable, and responsive to contextual constraints. This has intensified debate around how best to balance scientific rigor with pragmatic relevance in study design selection. AIM: This review aimed to provide a practical, design-oriented guide to selecting appropriate study designs for real-world health research by clarifying how rigor and pragmatism can be balanced without compromising methodological integrity. METHODS: A narrative methodological approach was conducted, synthesising peer-reviewed and grey literature on study design selection, pragmatic trials, implementation research, and real-world evidence. Searches were undertaken in MEDLINE, Scopus, and Web of Science, alongside guidance from major health research institutions. Evidence was analysed thematically, with study designs compared according to rigor, feasibility, transferability, and ethical and operational considerations. RESULTS: The review demonstrates that study designs occupy positions along a rigor-pragmatism continuum rather than discrete methodological hierarchies. Highly controlled designs offer strong causal inference but limited applicability, while pragmatic, hybrid, and observational designs enhance relevance and policy utility when rigorously applied. Contextual factors including health system capacity, ethical feasibility, resource availability, population vulnerability, and implementation stage emerge as critical drivers of appropriate design choice. CONCLUSION: Balancing rigor and pragmatism through context-sensitive design selection can generate more usable evidence, strengthen policy impact, and improve population health outcomes.