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

被引:43
作者
Freebairn, Louise [1 ,2 ,3 ]
Rychetnik, Lucie [2 ,3 ]
Atkinson, Jo-An [2 ,4 ]
Kelly, Paul [1 ,2 ,5 ]
McDonnell, Geoff [2 ,6 ]
Roberts, Nick [2 ]
Whittall, Christine [7 ]
Redman, Sally [2 ]
机构
[1] ACT Govt, Hlth Directorate, GPO Box 825, Canberra, ACT 2601, Australia
[2] Australian Prevent Partnership Ctr, Sax Inst, POB K617, Haymarket, NSW 1240, Australia
[3] Univ Notre Dame, Sch Med, POB 944, Broadway, NSW 2007, Australia
[4] Univ Sydney, Sydney Med Sch, Sydney, NSW 2006, Australia
[5] Australian Natl Univ, Canberra, ACT 2601, Australia
[6] Adapt Care Syst, Sydney, NSW 2052, Australia
[7] NSW Minist Hlth, LMB 961 North, Sydney, NSW 2059, Australia
基金
英国医学研究理事会;
关键词
Participatory dynamic simulation modelling; Decision support; Knowledge mobilisation; Childhood obesity; Alcohol; Diabetes in pregnancy; PUBLIC-HEALTH POLICY; SCIENCE METHODS; TRANSLATION; CARE; EXCHANGE; THINKING; COPRODUCTION; STAKEHOLDERS; TIME; TOOL;
D O I
10.1186/s12961-017-0245-1
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
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.
引用
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页数:12
相关论文
共 78 条
[71]   Modelling with stakeholders - Next generation [J].
Voinov, Alexey ;
Kolagani, Nagesh ;
McCall, Michael K. ;
Glynn, Pierre D. ;
Kragt, Marit E. ;
Ostermann, Frank O. ;
Pierce, Suzanne A. ;
Ramu, Palaniappan .
ENVIRONMENTAL MODELLING & SOFTWARE, 2016, 77 :196-220
[72]  
Voinov A, 2010, ENVIRON MODELL SOFTW, V25, P1268, DOI 10.1016/j.envsoft.2010.03.007
[73]   Exploring knowledge exchange: A useful framework for practice and policy [J].
Ward, Vicky ;
Smith, Simon ;
House, Allan ;
Hamer, Susan .
SOCIAL SCIENCE & MEDICINE, 2012, 74 (03) :297-304
[74]   Beyond two communities - from research utilization and knowledge translation to co-production? [J].
Wehrens, R. .
PUBLIC HEALTH, 2014, 128 (06) :545-551
[75]  
WHO, 2014, GLOBAL STATUS REPORT ON VIOLENCE PREVENTION 2014, P1
[76]   Transfer in context: Replication and adaptation in knowledge transfer relationships [J].
Williams, Charles .
STRATEGIC MANAGEMENT JOURNAL, 2007, 28 (09) :867-889
[77]   Systems thinking for transformational change in health [J].
Willis, Cameron D. ;
Best, Allan ;
Riley, Barbara ;
Herbert, Carol P. ;
Millar, John ;
Howland, David .
EVIDENCE & POLICY, 2014, 10 (01) :113-126
[78]   Translating Evidence into Population Health Improvement: Strategies and Barriers [J].
Woolf, Steven H. ;
Purnell, Jason Q. ;
Simon, Sarah M. ;
Zimmerman, Emily B. ;
Camberos, Gabriela J. ;
Haley, Amber ;
Fields, Robert P. .
ANNUAL REVIEW OF PUBLIC HEALTH, VOL 36, 2015, 36 :463-482