Collaborative learning framework for online stakeholder engagement.
Collaborative learning framework for online stakeholder engagement.
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DOI：
10.1111/hex.12383
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发表时间：
2016-08
期刊：
影响因子：
--
通讯作者：
Dalal S
中科院分区：
文献类型：
--
作者：
Khodyakov D;Savitsky TD;Dalal S
公众和利益相关者的参与可以提高研究和政策决策的质量。然而，这种接触在收集和分析来自大型、不同群体的意见方面提出了重大的方法挑战。解释在线方法如何促进利益攸关方的迭代参与，描述如何分析来自大型和不同利益攸关方群体的投入，并提出一个协作学习框架(CLF)来解释利益攸关方参与的结果。我们使用《关于减轻美国自杀负担的全国对话》作为在线利益相关者参与的案例研究，并使用贝叶斯数据建模方法来开发CLF。我们的数据建模结果确定了六个不同的利益相关者集群，它们在个人表达和群体一致的程度上存在差异，并表现出三种学习风格之一：向共识学习、通过对比学习和群体思维。在这项研究中，对比学习是最常见的，或者说占主导地位的学习方式。研究结果被用来开发CLF，它有助于探索众多利益相关者的观点；确定具有相似信念转变的参与者群；提供经验性的参与质量指标；并帮助确定主导的学习风格。通过对比检测学习的能力有助于说明利益相关者视角的差异，这可能有助于政策制定者，包括以患者为中心的结果研究所，通过征求和纳入患者、护理人员、医疗保健提供者和研究人员的意见来做出更好的决策。研究结果对于征求和纳入具有不同兴趣和视角的利益攸关方的意见具有重要意义。
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. 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. 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. 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. 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.
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影响因子：
6.6
作者：
Greenhalgh, Trisha;Russell, Jill;Parsons, Wayne
通讯作者：
Parsons, Wayne
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作者：
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通讯作者：
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