Abstract

Many recommendations and innovative approaches are available for the development and evaluation of complex health interventions. We investigated the dimensions of complexity described in health research and how these descriptions may affect the adopted research methodology (e.g., the choice of designs and methods). We used a mixed method approach to review the scientific literature evaluating complex interventions in the health field. Of 438 articles identified, 179 were subjected to descriptive analysis and 48 to content analysis. The three principal dimensions of complexity were: stakeholder characteristics, intervention multimodality and context. Recognition of such dimensions influenced the methodological choices made during evaluation of the interventions with their use of designs and methods, which aimed to address the complexity. We analysed not only how researchers view complexity but also the effects of such views on researcher practices. Our results highlight the need for clarification of what complexity means and to consider complexity when deciding how to evaluate research interventions.

Highlights

  • Complex interventions [1,2] challenge both researchers and stakeholders in terms of development and evaluation [3]

  • The research strategy was designed to identify articles written by authors who evaluate complex interventions in the field of health [15]

  • Of the 179 articles included in the study, 33.5% were protocols, 33.5% were pilot or feasibility studies and 33% were evaluation studies

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Summary

Introduction

Complex interventions [1,2] challenge both researchers and stakeholders in terms of development and evaluation [3] These interventions pose challenges when transferring them to different contexts and scaling them up or out [4,5,6]. Complexity involves the interaction of multiple components, the behaviour of those providing and receiving the intervention, the organisational level targeted, the variability in outcomes and the flexibility of the intervention. Another approach to analysing complexity uses complex-systems thinking [9,12].

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