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Abstract
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BACKGROUND: Public and stakeholder engagement can improve the quality of both research and policy decision making. However, such engagement poses significant methodological challenges in terms of collecting and analysing input from large, diverse groups.
OBJECTIVE: To explain how online approaches can facilitate iterative stakeholder engagement, to describe how input from large and diverse stakeholder groups can be analysed and to propose a collaborative learning framework (CLF) to interpret stakeholder engagement results.
METHODS: We use 'A National Conversation on Reducing the Burden of Suicide in the United States' as a case study of online stakeholder engagement and employ a Bayesian data modelling approach to develop a CLF.
RESULTS: Our data modelling results identified six distinct stakeholder clusters that varied in the degree of individual articulation and group agreement and exhibited one of the three learning styles: learning towards consensus, learning by contrast and groupthink. Learning by contrast was the most common, or dominant, learning style in this study.
CONCLUSION: Study results were used to develop a CLF, which helps explore multitude of stakeholder perspectives; identifies clusters of participants with similar shifts in beliefs; offers an empirically derived indicator of engagement quality; and helps determine the dominant learning style. The ability to detect learning by contrast helps illustrate differences in stakeholder perspectives, which may help policymakers, including Patient-Centered Outcomes Research Institute, make better decisions by soliciting and incorporating input from patients, caregivers, health-care providers and researchers. Study results have important implications for soliciting and incorporating input from stakeholders with different interests and perspectives.
Language: en |