Evidence map›Paper›PMID 30541523›Full record

ArticleBMC medical informatics and decision making2018

Decision makers' experience of participatory dynamic simulation modelling: methods for public health policy.

Louise Freebairn, Jo-An Atkinson, Paul M Kelly, Geoff McDonnell, Lucie Rychetnik

Abstract read
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In one paragraph

Article in BMC medical informatics and decision making, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

39 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Louise FreebairnACT Health, GPO Box 825, Canberra, ACT, 2601, Australia. louise.freebairn@act.gov.au.ORCID 0000-0002-7434-870X
Jo-An AtkinsonThe Australian Prevention Partnership Centre, Sax Institute, PO Box K617, Haymarket, Sydney, NSW, 1240, Australia.
Paul M KellyACT Health, GPO Box 825, Canberra, ACT, 2601, Australia.
Geoff McDonnellDecision Analytics, Sax Institute, Sydney, Australia.
Lucie RychetnikThe Australian Prevention Partnership Centre, Sax Institute, PO Box K617, Haymarket, Sydney, NSW, 1240, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSystems science methods such as dynamic simulation modelling are well suited to address questions about public health policy as they consider the complexity, context and dynamic nature of system-wide behaviours. Advances in technology have led to increased accessibility and interest in systems methods to address complex health policy issues. However, the involvement of policy decision makers in health-related simulation model development has been lacking. Where end-users have been included, there has been limited examination of their experience of the participatory modelling process and their views about the utility of the findings. This paper reports the experience of end-user decision makers, including senior public health policy makers and health service providers, who participated in three participatory simulation modelling for health policy case studies (alcohol related harm, childhood obesity prevention, diabetes in pregnancy), and their perceptions of the value and efficacy of this method in an applied health sector context.

methodsSemi-structured interviews were conducted with end-user participants from three participatory simulation modelling case studies in Australian real-world policy settings. Interviewees were employees of government agencies with jurisdiction over policy and program decisions and were purposively selected to include perspectives at different stages of model development.

resultsThe 'co-production' aspect of the participatory approach was highly valued. It was reported as an essential component of building understanding of the modelling process, and thus trust in the model and its outputs as a decision-support tool. The unique benefits of simulation modelling included its capacity to explore interactions of risk factors and combined interventions, and the impact of scaling up interventions. Participants also valued simulating new interventions prior to implementation in the real world, and the comprehensive mapping of evidence and its gaps to prioritise future research. The participatory aspect of simulation modelling was time and resource intensive and therefore most suited to high priority complex topics with contested options for intervening.

conclusionThese findings highlight the value of a participatory approach to dynamic simulation modelling to support its utility in applied health policy settings.

Indexed as

Decision MakingGovernment AgenciesHealth PolicyModels, TheoreticalPolicy MakingPublic HealthAlcohol-Related DisordersAustraliaDiabetes, GestationalFemaleHumansPediatric ObesityPregnancyPregnancy in DiabeticsAlcoholChildhood obesityDecision supportDiabetes in pregnancyDynamic simulation modellingGestational diabetesHybrid modellingKnowledge mobilisationMultimethod modellingParticipatory modellingPrevention policyPublic health

Identifiers

PMID30541523
PMCPMC6291959

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.