Using stated preference methods to facilitate knowledge translation in implementation science.
Implement Sci Commun
; 5(1): 32, 2024 Mar 28.
Article
em En
| MEDLINE
| ID: mdl-38549129
ABSTRACT
Enhancing the arsenal of methods available to shape implementation strategies and bolster knowledge translation is imperative. Stated preference methods, including discrete choice experiments (DCE) and best-worst scaling (BWS), rooted in economics, emerge as robust, theory-driven tools for understanding and influencing the behaviors of both recipients and providers of innovation. This commentary outlines the wide-ranging application of stated preference methods across the implementation continuum, ushering in effective knowledge translation. The prospects for utilizing these methods within implementation science encompass (1) refining and tailoring intervention and implementation strategies, (2) exploring the relative importance of implementation determinants, (3) identifying critical outcomes for key decision-makers, and 4) informing policy prioritization. Operationalizing findings from stated preference research holds the potential to precisely align health products and services with the requisites of patients, providers, communities, and policymakers, thereby realizing equitable impact.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
Implement Sci Commun
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Estados Unidos