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Abstract：
BACKGROUND: Without a robust evidence base to support recommendations for first aid, health, and medical services at mass gatherings (MGs), levels of care will continue to vary. Streamlining and standardizing post-event reporting for MG medical services could improve inter-event comparability, and prospectively influence event safety and planning through the application of a research template, thereby supporting and promoting growth of the evidence base and the operational safety of this discipline. Understanding the relationships between categories of variables is key. The present paper is focused on theory building, providing an evolving conceptual model, laying the groundwork for exploring the relationships between categories of variables pertaining the health outcomes of MGs.
METHODS: A content analysis of 54 published post-event medical case reports, including a comparison of the features of published data models for MG health outcomes.
RESULTS: A layered model of essential conceptual components for post-event medical reporting is presented as the Data Reporting, Evaluation, & Analysis for Mass-Gathering Medicine (DREAM) model. This model is relational and embeds data domains, organized operationally, into \"inputs,\" \"modifiers,\" \"actuals,\" and \"outputs\" and organized temporally into pre-, during, post-event, and reporting phases.
CONCLUSIONS: Situating the DREAM model in relation to existing models for data collection vis a vis health outcomes, the authors provide a detailed discussion on similarities and points of difference.
CONCLUSIONS: Currently, data collection and analysis related to understanding health outcomes arising from MGs is not informed by robust conceptual models. This paper is part of a series of nested papers focused on the future state of post-event medical reporting.
METHODS: A content analysis of 54 published post-event medical case reports, including a comparison of the features of published data models for MG health outcomes.
RESULTS: A layered model of essential conceptual components for post-event medical reporting is presented as the Data Reporting, Evaluation, & Analysis for Mass-Gathering Medicine (DREAM) model. This model is relational and embeds data domains, organized operationally, into \"inputs,\" \"modifiers,\" \"actuals,\" and \"outputs\" and organized temporally into pre-, during, post-event, and reporting phases.
CONCLUSIONS: Situating the DREAM model in relation to existing models for data collection vis a vis health outcomes, the authors provide a detailed discussion on similarities and points of difference.
CONCLUSIONS: Currently, data collection and analysis related to understanding health outcomes arising from MGs is not informed by robust conceptual models. This paper is part of a series of nested papers focused on the future state of post-event medical reporting.
摘要：
背景：没有强有力的证据基础来支持急救建议，健康,和群众集会(MGs)的医疗服务，护理水平将继续变化。简化和标准化MG医疗服务的事件后报告可以提高事件间的可比性，并通过应用研究模板来前瞻性地影响事件的安全性和计划，从而支持和促进证据基础的增长和该学科的操作安全。理解变量类别之间的关系是关键。本文的重点是理论建设，提供一个不断发展的概念模型，为探索与MGs健康结果相关的变量类别之间的关系奠定基础。
方法：对54份已发表的事件后医学病例报告进行内容分析，包括已发布的MG健康结果数据模型的特征比较。
结果：事件后医疗报告的基本概念组成部分的分层模型作为数据报告，评价,质量聚集医学(DREAM)模型分析。此模型是关系模型，并嵌入数据域，有组织的操作，进入“输入，\"\"修饰符,\"\"实际值,\"和\"输出\"，并按时间组织到前，during,事后,报告阶段。
结论：将DREAM模型与现有的数据收集模型相对于健康结果，作者对相同点和不同点进行了详细的讨论。
结论：目前，与了解MGs产生的健康结果相关的数据收集和分析不能通过稳健的概念模型获得。本文是一系列嵌套论文的一部分，重点是事件后医学报告的未来状态。
方法：对54份已发表的事件后医学病例报告进行内容分析，包括已发布的MG健康结果数据模型的特征比较。
结果：事件后医疗报告的基本概念组成部分的分层模型作为数据报告，评价,质量聚集医学(DREAM)模型分析。此模型是关系模型，并嵌入数据域，有组织的操作，进入“输入，\"\"修饰符,\"\"实际值,\"和\"输出\"，并按时间组织到前，during,事后,报告阶段。
结论：将DREAM模型与现有的数据收集模型相对于健康结果，作者对相同点和不同点进行了详细的讨论。
结论：目前，与了解MGs产生的健康结果相关的数据收集和分析不能通过稳健的概念模型获得。本文是一系列嵌套论文的一部分，重点是事件后医学报告的未来状态。
