To report on the findings from a national survey of UK intensive care units (ICUs) exploring nurse staffing models currently in use and changes since COVID-19.

A survey was designed and distributed using a web-based platform to senior unit leads via Intensive care national audit & research centre contacts.

Senior nurses representing the 331 National Health Service adult ICUs across the UK (across 231 hospitals/155 trusts), including the Channel Islands and Isle of Man.

A 15-item survey.

A total of 196 survey responses representing 300 units, majority general and single units, resulting in a 90.6% unit-level response rate. ICU unit characteristics included the average number of total, level 3 and level 2 critical care beds of 26.36 (SD=21.48), 15.67 (SD=15.33) and 10.96 (SD=8.86), respectively. Most units reported nurse to patient ratios compliant with national guidelines and service specifications. Post-COVID-19 changes to ICU nurse staffing establishments were reported by 44% respondents, including increases in non-registered staff. However, limited data were provided regarding decision-making around and changes to bedside allocation of nurses since COVID-19.

Increased numbers and use of non-registered staff within the ICU is indicative of an alternative staffing model to address nursing shortages. However, more research is needed to understand how this staffing group is being used compared with, and alongside, registered nurses.

Clinicaltrials.gov: NCT05917574.

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.