Rugged landscapes: complexity and implementation science.
Background: Mis-implementation-defined as failure to successfully implement and continue evidence-based programs-is widespread in public health practice. Yet the causes of this phenomenon are poorly understood.Methods: We develop an agent-based computational model to explore how complexity hinders e...
| Publicado en: | Implementation Science Vol. 15; no. 1 |
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| Autores principales: | , , , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
9/29/2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=146149663&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 146149663 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17485908 38MN jtl: Implementation Science issn: 17485908 maglogo: N pubinfo: dt: 9/29/2020 vid: 15 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 146149663 146149663 NLM32993756 146149663 10.1186/s13012-020-01028-5 NLM32993756 146149663 ppct: 1 formats: tig: atl: Rugged landscapes: complexity and implementation science. aug: au: Ornstein, Joseph T. Hammond, Ross A. Padek, Margaret Mazzucca, Stephanie Brownson, Ross C. affil: Brown School, Washington University in St. Louis, Brookings Drive, St. Louis, MO, USA sug: subj: Professional Practice, Evidence-Based Public Health Human Decision Making Comparative Studies Multicenter Studies Evaluation Research Validation Studies Ferrans and Powers Quality of Life Index ab: Background: Mis-implementation-defined as failure to successfully implement and continue evidence-based programs-is widespread in public health practice. Yet the causes of this phenomenon are poorly understood.Methods: We develop an agent-based computational model to explore how complexity hinders effective implementation. The model is adapted from the evolutionary biology literature and incorporates three distinct complexities faced in public health practice: dimensionality, ruggedness, and context-specificity. Agents in the model attempt to solve problems using one of three approaches-Plan-Do-Study-Act (PDSA), evidence-based interventions (EBIs), and evidence-based decision-making (EBDM).Results: The model demonstrates that the most effective approach to implementation and quality improvement depends on the underlying nature of the problem. Rugged problems are best approached with a combination of PDSA and EBI. Context-specific problems are best approached with EBDM.Conclusions: The model's results emphasize the importance of adapting one's approach to the characteristics of the problem at hand. Evidence-based decision-making (EBDM), which combines evidence from multiple independent sources with on-the-ground local knowledge, is a particularly potent strategy for implementation and quality improvement. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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