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协作式视觉分析:一种预防伤害的健康分析方法。

Collaborative Visual Analytics: A Health Analytics Approach to Injury Prevention.

作者信息

Al-Hajj Samar, Fisher Brian, Smith Jennifer, Pike Ian

机构信息

Faculty of Health Sciences, American University of Beirut, Beirut 1107 2020, Lebanon.

School of Interactive Arts and Technology, Simon Fraser University, Surrey, BC V3T 0A3, Canada.

出版信息

Int J Environ Res Public Health. 2017 Sep 12;14(9):1056. doi: 10.3390/ijerph14091056.

DOI:10.3390/ijerph14091056
PMID:28895928
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5615593/
Abstract

: Accurate understanding of complex health data is critical in order to deal with wicked health problems and make timely decisions. Wicked problems refer to ill-structured and dynamic problems that combine multidimensional elements, which often preclude the conventional problem solving approach. This pilot study introduces visual analytics (VA) methods to multi-stakeholder decision-making sessions about child injury prevention; : Inspired by the Delphi method, we introduced a novel methodology-group analytics (GA). GA was pilot-tested to evaluate the impact of collaborative visual analytics on facilitating problem solving and supporting decision-making. We conducted two GA sessions. Collected data included stakeholders' observations, audio and video recordings, questionnaires, and follow up interviews. The GA sessions were analyzed using the Joint Activity Theory protocol analysis methods; : The GA methodology triggered the emergence of '' among stakeholders. This evolved throughout the sessions to enhance stakeholders' verbal and non-verbal communication, as well as coordination of joint activities and ultimately collaboration on problem solving and decision-making; : Understanding complex health data is necessary for informed decisions. Equally important, in this case, is the use of the group analytics methodology to achieve ' among diverse stakeholders about health data and their implications.

摘要

为应对棘手的健康问题并及时做出决策,准确理解复杂的健康数据至关重要。棘手问题是指那些结构不良且动态变化的问题,它们融合了多维度元素,常常使传统的问题解决方法无法适用。这项试点研究将视觉分析(VA)方法引入关于儿童伤害预防的多利益相关方决策会议中;受德尔菲法启发,我们引入了一种新颖的方法——群体分析(GA)。对GA进行了试点测试,以评估协作式视觉分析对促进问题解决和支持决策的影响。我们开展了两次GA会议。收集的数据包括利益相关方的观察结果、音频和视频记录、问卷以及后续访谈。使用联合活动理论协议分析方法对GA会议进行了分析;GA方法引发了利益相关方之间“ ”的出现。这种情况在会议过程中不断演变,以加强利益相关方的言语和非言语交流,以及联合活动的协调,最终促进在问题解决和决策方面的合作;为做出明智决策,理解复杂的健康数据是必要的。在这种情况下,同样重要的是使用群体分析方法,以在不同利益相关方之间就健康数据及其影响达成“ ”。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a836/5615593/b3c633e46e76/ijerph-14-01056-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a836/5615593/21cf52999c95/ijerph-14-01056-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a836/5615593/b05e3d4ac57a/ijerph-14-01056-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a836/5615593/6053532a329a/ijerph-14-01056-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a836/5615593/d277d37a93dd/ijerph-14-01056-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a836/5615593/b5b771c160b0/ijerph-14-01056-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a836/5615593/26576e86377c/ijerph-14-01056-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a836/5615593/b3c633e46e76/ijerph-14-01056-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a836/5615593/21cf52999c95/ijerph-14-01056-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a836/5615593/b05e3d4ac57a/ijerph-14-01056-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a836/5615593/6053532a329a/ijerph-14-01056-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a836/5615593/d277d37a93dd/ijerph-14-01056-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a836/5615593/b5b771c160b0/ijerph-14-01056-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a836/5615593/26576e86377c/ijerph-14-01056-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a836/5615593/b3c633e46e76/ijerph-14-01056-g007.jpg

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本文引用的文献

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The Canadian Atlas of Child and Youth Injury: Mobilizing Injury Surveillance Data to Launch a National Knowledge Translation Tool.《加拿大儿童和青少年伤害地图集:利用伤害监测数据推出全国性知识转化工具》
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Digital dashboard design using multiple data streams for disease surveillance with influenza surveillance as an example.
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JMIR Res Protoc. 2019 Oct 28;8(10):e14019. doi: 10.2196/14019.
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