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Health Res Policy Syst. 2017 Oct 2;15(1):83. doi: 10.1186/s12961-017-0245-1.

Knowledge mobilisation for policy development: implementing systems approaches through participatory dynamic simulation modelling.

Author information

1
ACT Government, Health Directorate, GPO Box 825, Canberra, ACT, 2601, Australia. louise.freebairn@act.gov.au.
2
The Australian Prevention Partnership Centre, Sax Institute, PO Box K617, Haymarket, NSW, 1240, Sydney, Australia. louise.freebairn@act.gov.au.
3
School of Medicine, University of Notre Dame, PO Box 944, Broadway, NSW, 2007, Sydney, Australia. louise.freebairn@act.gov.au.
4
The Australian Prevention Partnership Centre, Sax Institute, PO Box K617, Haymarket, NSW, 1240, Sydney, Australia.
5
School of Medicine, University of Notre Dame, PO Box 944, Broadway, NSW, 2007, Sydney, Australia.
6
Sydney Medical School, University of Sydney, Sydney, NSW, 2006, Australia.
7
ACT Government, Health Directorate, GPO Box 825, Canberra, ACT, 2601, Australia.
8
The Australian National University, Canberra, ACT, 2601, Australia.
9
Adaptive Care Systems, Sydney, NSW, 2052, Australia.
10
NSW Ministry of Health, LMB 961 North, Sydney, NSW, 2059, Australia.

Abstract

BACKGROUND:

Evidence-based decision-making is an important foundation for health policy and service planning decisions, yet there remain challenges in ensuring that the many forms of available evidence are considered when decisions are being made. Mobilising knowledge for policy and practice is an emergent process, and one that is highly relational, often messy and profoundly context dependent. Systems approaches, such as dynamic simulation modelling can be used to examine both complex health issues and the context in which they are embedded, and to develop decision support tools.

OBJECTIVE:

This paper reports on the novel use of participatory simulation modelling as a knowledge mobilisation tool in Australian real-world policy settings. We describe how this approach combined systems science methodology and some of the core elements of knowledge mobilisation best practice. We describe the strategies adopted in three case studies to address both technical and socio-political issues, and compile the experiential lessons derived. Finally, we consider the implications of these knowledge mobilisation case studies and provide evidence for the feasibility of this approach in policy development settings.

CONCLUSION:

Participatory dynamic simulation modelling builds on contemporary knowledge mobilisation approaches for health stakeholders to collaborate and explore policy and health service scenarios for priority public health topics. The participatory methods place the decision-maker at the centre of the process and embed deliberative methods and co-production of knowledge. The simulation models function as health policy and programme dynamic decision support tools that integrate diverse forms of evidence, including research evidence, expert knowledge and localised contextual information. Further research is underway to determine the impact of these methods on health service decision-making.

KEYWORDS:

Alcohol; Childhood obesity; Decision support; Diabetes in pregnancy; Knowledge mobilisation; Participatory dynamic simulation modelling

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