Irie Whitney C, Kerkhoff Andrew, Kim Hae-Young, Geng Elvin, Eshun-Wilson Ingrid
School of Social Work, Boston College, Chestnut Hill, MA, USA.
Division of HIV, Infectious Diseases and Global Medicine Zuckerberg San Francisco General Hospital and Trauma Center, University of California, San Francisco, San Francisco, CA, USA.
Implement Sci Commun. 2024 Mar 28;5(1):32. doi: 10.1186/s43058-024-00554-3.
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.
增强用于制定实施策略和加强知识转化的方法库势在必行。源自经济学的陈述偏好方法，包括离散选择实验（DCE）和最佳-最差尺度法（BWS），已成为用于理解和影响创新接受者和提供者行为的强大的、理论驱动的工具。本评论概述了陈述偏好方法在整个实施连续体中的广泛应用，从而实现有效的知识转化。在实施科学中使用这些方法的前景包括：（1）完善和定制干预及实施策略；（2）探索实施决定因素的相对重要性；（3）确定关键决策者的关键结果；以及（4）为政策优先排序提供信息。将陈述偏好研究的结果付诸实践有可能使健康产品和服务与患者、提供者、社区及政策制定者的需求精确匹配，从而实现公平影响。
