为 COVID-19 制定研究方案,评估将信息新鲜度纳入在线资源网络分析的新措施。

Meredith Abrams, Audrey Wong, Hanae El Kholti, Yunro Chung, Lisa Armitige, Dongwen Wang
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引用次数: 0

摘要

我们提出了一种新的测量方法--新鲜度整合连接度(DCIF),用于将信息新鲜度纳入在线资源网络分析。我们在 COVID-19 风险评估相关的在线信息资源数据集上进行了试点研究。在 52 个节点中,我们发现 DCIF 的数值与传统的结构性测量指标 "连接度"(Degree of Connectivity,DC)之间存在显著的统计学差异。对所选的 18 个节点进行的人工审核显示,DCIF 在其中 11 个节点中的表现优于 DC,这表明所提议的新测量方法具有潜在的前景。我们在试点的基础上最终确定了人工审核协议,并开始了全面研究。所提出的新方法有可能对信息新鲜度进行量化评估,从而及时有效地传播临床证据。我们还需要进一步研究,以解决这项试点研究的局限性,并检验研究结果的普遍性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development of a Study Protocol for Evaluation of a Novel Measure to Incorporate Information Freshness into Network Analysis of Online Resources for COVID-19.

We proposed a novel measure, Degree of Connectivity with Integration of Freshness (DCIF), to incorporate information freshness into analysis of online resource networks. We conducted a pilot study to apply this new measure to a dataset of online information resources related to COVID-19 risk assessment. Among the 52 nodes, we recorded statistically significant difference between the numerical values of DCIF and the traditional structural measure Degree of Connectivity (DC). Manual reviews of 18 selected nodes showed that DCIF outperformed DC in 11 of them, suggesting potential promise of the proposed new measure. We finalized the protocol for manual review based on the pilot and started a full-scale study. The proposed new measure has the potential to provide quantitative assessment on information freshness for timely and effective dissemination of clinical evidence. Further research is required to address the limitations of this pilot study and to examine the generalization of the findings.

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