Implementation science communications · 2026
Preferences for rapid implementation science: a real-time discrete choice experiment.
СтатьяНазвание и аннотация: Перевод в очереди
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Geng Elvin H, Ramaswamy Rohit, Ramsey Alex T, Kanyike Andrew M, Eboreime Ejemai, Sturke Rachel, Vorkoper Susan, Le Tourneau Noelle
Аннотация
BACKGROUND: A persistent challenge in implementation science is the gap between the pace and priorities of academic research and the needs of decision-makers. While traditional research emphasizes methodological rigor, policymakers often require timely, relevant, and stakeholder-engaged evidence to inform decisions.
METHODS: We conducted a real-time discrete choice experiment (DCE) with participants at an implementation science conference in Washington, D.C. during a 75-minute session on rapid implementation science. At the midpoint of the panel, the audience were invited to participate in a 10-minute DCE based on a vignette describing a hypothetical study on implementing a new vaccine facing public hesitancy. Choice sets included six attributes: rapidity of study, study design, primary outcome, level of community engagement, leadership, and costs. We used a mixed logit model to analyze preferences in real time and presented results back to the audience at the end of the session.
RESULTS: Ninety-four participants completed the DCE; all reported working in research and 83% reported working in program implementation. Respondents placed high value on timing of results, with strong preferences for receiving results in 6 months compared 12 months (β=-1.1, 95% CI: - 1.6- - 0.6; p < 0.001) and 18 months (β=-1.7,-2.3- - 1.0; p < 0.001). Participants preferred the primary outcome to be vaccine uptake rather than vaccine acceptability (β = 1.7, 1.1-2.2; p < 0.001) and the study to be community-engaged rather than expert-led (β = 1.6, 1.0-2.2; p < 0.001). We found no preference for randomization compared to before and after (p = 0.88), nor leadership by the ministry of health versus academic institutions (p = 0.12). Respondents were willing to wait 11.3 (95% CI 7.0-15.5) additional months for a program developed with engaged stakeholders compared to expert-driven (p < 0.001) and 11.9 months (95% CI 8.1-15.8) for results on vaccine uptake rather than acceptability (p < 0.001).
CONCLUSIONS: This real-time DCE demonstrated that with adequate preparation, it is feasible-to generate robust, interpretable, and actionable results within an hour. Findings challenge the assumption that randomization is the highest priority in research design, highlighting instead the importance of outcome and community engagement. By aligning more closely with the values of decision-makers, rapid science approaches such as real-time DCEs may help bridge the research-policy gap.
METHODS: We conducted a real-time discrete choice experiment (DCE) with participants at an implementation science conference in Washington, D.C. during a 75-minute session on rapid implementation science. At the midpoint of the panel, the audience were invited to participate in a 10-minute DCE based on a vignette describing a hypothetical study on implementing a new vaccine facing public hesitancy. Choice sets included six attributes: rapidity of study, study design, primary outcome, level of community engagement, leadership, and costs. We used a mixed logit model to analyze preferences in real time and presented results back to the audience at the end of the session.
RESULTS: Ninety-four participants completed the DCE; all reported working in research and 83% reported working in program implementation. Respondents placed high value on timing of results, with strong preferences for receiving results in 6 months compared 12 months (β=-1.1, 95% CI: - 1.6- - 0.6; p < 0.001) and 18 months (β=-1.7,-2.3- - 1.0; p < 0.001). Participants preferred the primary outcome to be vaccine uptake rather than vaccine acceptability (β = 1.7, 1.1-2.2; p < 0.001) and the study to be community-engaged rather than expert-led (β = 1.6, 1.0-2.2; p < 0.001). We found no preference for randomization compared to before and after (p = 0.88), nor leadership by the ministry of health versus academic institutions (p = 0.12). Respondents were willing to wait 11.3 (95% CI 7.0-15.5) additional months for a program developed with engaged stakeholders compared to expert-driven (p < 0.001) and 11.9 months (95% CI 8.1-15.8) for results on vaccine uptake rather than acceptability (p < 0.001).
CONCLUSIONS: This real-time DCE demonstrated that with adequate preparation, it is feasible-to generate robust, interpretable, and actionable results within an hour. Findings challenge the assumption that randomization is the highest priority in research design, highlighting instead the importance of outcome and community engagement. By aligning more closely with the values of decision-makers, rapid science approaches such as real-time DCEs may help bridge the research-policy gap.