We report the collaborative views of a group of nurses, midwives and allied health professionals (NMAHPs) in the UK who have a genomics research remit or interest. Our group includes genetic counsellors under this diverse category of healthcare workers.

This group came together as part of the National Institute for Health and Social Care Research (NIHR) Genomics Research National Specialty Group. After responding to a survey to elicit the views of NMAHPs working in genomics, some of the original 45 respondents, along with others who learnt of the project by word of mouth, have worked together to produce this article.

The paper aims to set out in clear terms the value of NMAHPs to research that supports the patient-centred implementation of genomics in the National Health Service (NHS).

We discuss four potential areas where NMAHPs, in particular, can contribute to the research. These are patient perspectives and epistemic justice, psychosocial impacts, the familial nature of genomics and equity. We argue that this group (NMAHPs) represents a potentially underused resource for the NHS as it seeks to ensure that advances in genomics are translated into patient benefit.

We propose that NMAHPs, with our research expertise, are well placed to shape and deliver a research agenda that explores models of patient-centred care in the genomics era. We call for increased funding for NMAHP research roles and funding opportunities to deliver this fundamental work.

The COVID-19 pandemic highlighted the significance of mathematical modelling in decision-making and the limited capacity in many low-income and middle-income countries (LMICs). Thus, we studied how modelling supported policy decision-making processes in LMICs during the pandemic (details in a separate paper).

We found that strong researcher–policymaker relationships and co-creation facilitated knowledge translation, while scepticism, political pressures and demand for quick outputs were barriers. We also noted that routine use of modelled evidence for decision-making requires sustained funding, capacity building for policy-facing modelling, robust data infrastructure and dedicated knowledge translation mechanisms.

These lessons helped us co-create a framework and policy roadmap for improving the routine use of modelling evidence in public health decision-making. This communication paper describes the framework components and provides an implementation approach and evidence for the recommendations. The components include (1) funding, (2) capacity building, (3) data infrastructure, (4) knowledge translation platforms and (5) a culture of evidence use.

Our framework integrates the supply (modellers) and demand (policymakers) sides and contextual factors that enable change. It is designed to be generic and disease-agnostic for any policy decision-making that modelling could support. It is not a decision-making tool but a guiding framework to help build capacity for evidence-based policy decision-making. The target audience is modellers and policymakers, but it could include other partners and implementers in public health decision-making.

The framework was created through engagements with policymakers and researchers and reflects their real-life experiences during the COVID-19 pandemic. Its purpose is to guide stakeholders, especially in lower-resourced settings, in building modelling capacity, prioritising efforts and creating an enabling environment for using models as part of the evidence base to inform public health decision-making. To validate its robustness and impact, further work is needed to implement and evaluate this framework in diverse settings.