Background

Social innovations in health are inclusive solutions to address the healthcare delivery gap that meet the needs of end users through a multi-stakeholder, community-engaged process. While social innovations for health have shown promise in closing the healthcare delivery gap, more research is needed to evaluate, scale up, and sustain social innovation. Research checklists can standardize and improve reporting of research findings, promote transparency, and increase replicability of study results and findings.

Methods and findings

The research checklist was developed through a 3-step community-engaged process, including a global open call for ideas, a scoping review, and a 3-round modified Delphi process. The call for entries solicited checklists and related items and was open between November 27, 2019 and February 1, 2020. In addition to the open call submissions and scoping review findings, a 17-item Social Innovation For Health Research (SIFHR) Checklist was developed based on the Template for Intervention Description and Replication (TIDieR) Checklist. The checklist was then refined during 3 rounds of Delphi surveys conducted between May and June 2020. The resulting checklist will facilitate more complete and transparent reporting, increase end-user engagement, and help assess social innovation projects. A limitation of the open call was requiring internet access, which likely discouraged participation of some subgroups.

Conclusions

The SIFHR Checklist will strengthen the reporting of social innovation for health research studies. More research is needed on social innovation for health.

The SARS-CoV-2 pandemic has had an unprecedented impact on multiple levels of society. Not only has the pandemic completely overwhelmed some health systems but it has also changed how scientific evidence is shared and increased the pace at which such evidence is published and consumed, by scientists, policymakers and the wider public. More significantly, the pandemic has created tremendous challenges for decision-makers, who have had to implement highly disruptive containment measures with very little empirical scientific evidence to support their decision-making process. Given this lack of data, predictive mathematical models have played an increasingly prominent role. In high-income countries, there is a long-standing history of established research groups advising policymakers, whereas a general lack of translational capacity has meant that mathematical models frequently remain inaccessible to policymakers in low-income and middle-income countries. Here, we describe a participatory approach to modelling that aims to circumvent this gap. Our approach involved the creation of an international group of infectious disease modellers and other public health experts, which culminated in the establishment of the COVID-19 Modelling (CoMo) Consortium. Here, we describe how the consortium was formed, the way it functions, the mathematical model used and, crucially, the high degree of engagement fostered between CoMo Consortium members and their respective local policymakers and ministries of health.