Collaborative learning framework for online stakeholder engagement.

Area of Science:

• Public Health Research
• Health Policy Decision Making
• Stakeholder Engagement Methodologies

Background:

• Public and stakeholder engagement are crucial for enhancing research and policy development.
• Collecting and analyzing input from large, diverse groups presents significant methodological challenges.

Purpose of the Study:

• To demonstrate how online approaches can support iterative stakeholder engagement.
• To outline methods for analyzing input from extensive and varied stakeholder groups.
• To introduce a collaborative learning framework (CLF) for interpreting stakeholder engagement outcomes.

Main Methods:

• Utilized 'A National Conversation on Reducing the Burden of Suicide in the United States' as a case study for online stakeholder engagement.
• Employed a Bayesian data modeling approach to develop the collaborative learning framework (CLF).

Main Results:

• Data modeling identified six distinct stakeholder clusters based on articulation and agreement levels.
• Three primary learning styles were observed: learning towards consensus, learning by contrast, and groupthink.
• Learning by contrast emerged as the most prevalent learning style in the study.

Conclusions:

• The developed CLF aids in exploring diverse stakeholder perspectives and identifying belief shifts.
• The framework provides an empirical indicator of engagement quality and dominant learning styles.
• Understanding 'learning by contrast' can inform policymakers, like the Patient-Centered Outcomes Research Institute, for better decision-making by incorporating varied stakeholder input.