Title |
Participatory simulation modelling to inform public health policy and practice: Rethinking the evidence hierarchies
|
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Published in |
Journal of Public Health Policy, April 2017
|
DOI | 10.1057/s41271-016-0061-9 |
Pubmed ID | |
Authors |
Eloise O’Donnell, Jo-An Atkinson, Louise Freebairn, Lucie Rychetnik |
Abstract |
Drawing on the long tradition of evidence-based medicine that aims to improve the efficiency and effectiveness of clinical practice, the field of public health has sought to apply 'hierarchies of evidence' to appraise and synthesise public health research. Various critiques of this approach led to the development of synthesis methods that include broader evidence typologies and more 'fit for purpose' privileging of methodological designs. While such adaptations offer great utility for evidence-informed public health policy and practice, this paper offers an alternative perspective on the synthesis of evidence that necessitates a yet more egalitarian approach. Dynamic simulation modelling is increasingly recognised as a valuable evidence synthesis tool to inform public health policy and programme planning for complex problems. The development of simulation models draws on and privileges a wide range of evidence typologies, thus challenging the traditional use of 'hierarchies of evidence' to support decisions on complex dynamic problems. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 1 | 33% |
Curaçao | 1 | 33% |
Unknown | 1 | 33% |
Demographic breakdown
Type | Count | As % |
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Practitioners (doctors, other healthcare professionals) | 2 | 67% |
Scientists | 1 | 33% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 51 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 12 | 24% |
Researcher | 5 | 10% |
Student > Master | 5 | 10% |
Student > Doctoral Student | 4 | 8% |
Professor > Associate Professor | 4 | 8% |
Other | 9 | 18% |
Unknown | 12 | 24% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 10 | 20% |
Social Sciences | 6 | 12% |
Nursing and Health Professions | 4 | 8% |
Agricultural and Biological Sciences | 3 | 6% |
Computer Science | 2 | 4% |
Other | 9 | 18% |
Unknown | 17 | 33% |