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Competency framework on simulation model-based decision-making for Master of Public Health students

BACKGROUND: Simulation models support decision-making by helping public health practitioners evaluate the potential effects of interventions in complex systems and their ultimate health impacts. However, many public health professionals remain unfamiliar with constructing simulation models and how b...

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Detalles Bibliográficos
Autores principales: Hrzic, R, Cade, M V, Wong, BLH, McCreesh, N, Simon, J, Czabanowska, K
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10595394/
http://dx.doi.org/10.1093/eurpub/ckad160.1479
Descripción
Sumario:BACKGROUND: Simulation models support decision-making by helping public health practitioners evaluate the potential effects of interventions in complex systems and their ultimate health impacts. However, many public health professionals remain unfamiliar with constructing simulation models and how best to use their outputs for decision-making due to a lack of training. This study developed a competency framework on simulation model-supported decision-making targeting Master of Public Health students. METHODS: The study was conducted in three phases: a literature review, a two-stage online Delphi survey, and an online consensus workshop. A draft competency framework was developed based on 28 peer-reviewed publications. A two-stage online Delphi survey involving experts in simulation modelling, health policy, and public health education was conducted to validate the domains and competencies in the framework. Finally, an online consensus workshop was held to evaluate the competency framework and discuss its implementation in education. RESULTS: The literature-based draft framework included twenty competencies related to project planning, stakeholder engagement, problem definition, evidence identification, participatory system mapping, model calibration, and the interpretation and dissemination of model results. The Delphi surveys included four and twelve European experts each and identified additional competencies in assessing uncertainty and generalisability. The online consensus workshop included six experts and recommended expertise and profile differentiation to enhance the framework's utility for public health training. CONCLUSIONS: The competency framework developed in this study is critical to including simulation model-supported decision-making in public health training. Developing self-assessment tools will refine the competency framework through expertise and profile differentiation and support developing appropriate educational interventions. KEY MESSAGES: • Public health professionals need training in simulation model-supported decision-making to evaluate the health impact of various interventions more effectively. • The developed competency framework is critical to including simulation model-supported decision-making in public health training but needs expertise and profile differentiation for implementation.