Related Experiment Video
Updated: Jun 29, 2025

The Participant-Reported Implementation Update and Score PRIUS: A Novel Method for Capturing Implementation-Related Data Over Time
Published on: February 19, 2021
Using stated preference methods to facilitate knowledge translation in implementation science
Whitney C Irie1, Andrew Kerkhoff2, Hae-Young Kim3
1School of Social Work, Boston College, Chestnut Hill, MA, USA. whitney.irie@bc.edu.
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.
More Related Videos
Related Concept Videos
Improving Translational Accuracy
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
On...
Bioequivalence: Overview
Analysis of Population Pharmacokinetic Data
The Scientific Method in Nursing Process
When using research findings to change practice, one must understand the process used to guide a study. The scientific method is a systematic, step-by-step process that supports the data's validity, reliability, and generalizability. As a result, findings can be...
Crossover Experiments
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.

