在证据综合中检测和减轻欺诈性或错误数据的工具,技术,方法和流程:范围审查协议
Timothy Hugh Barker1, Grace McKenzie McBride1, Amanda Ross-White2
1Health Evidence Synthesis, Recommendations and Impact (HESRI), School of Public Health, The University of Adelaide, Adelaide, SA, Australia.
JBI evidence synthesis
|September 10, 2024
概括
本范围审查确定了在证据综合中检测和减轻欺诈数据的方法. 它旨在通过确保系统审查中使用的科学文献的完整性来保护决策.
科学领域:
- 证据综合 证据综合
- 研究诚信研究诚信
- 数据质量管理数据质量管理
背景情况:
- 基于证据的决策依赖于高质量的科学文献,通常通过系统审查进行合成.
- 掠夺性期刊的激增引入了欺诈性或错误的数据,可能危及证据综合和随后的政策/实践决策.
- 确保证据综合的可靠性对于保持对科学研究的信任至关重要.
研究的目的:
- 在证据综合中识别和分类用于检测欺诈或错误数据的工具,技术,方法和流程.
- 描述科学文献中减轻泄露数据影响的现有方法.
- 为维护证据综合中的数据完整性的策略提供全面的概述.
主要方法:
- 一种范围审查方法,遵守JBI指南和PRISMA-ScR报告标准.
- 包括同行评审的文章,评论,书籍,社论和指导文件,描述数据欺诈检测或缓解技术.
- 标题,摘要和全文的重复选,然后进行数据提取和描述性合成.
主要成果:
- 列出了一系列推和应用的方法来识别欺诈性或错误的数据.
- 描述了旨在最大限度地减少受损数据对证据综合结果影响的技术.
- 介绍了确定提高数据可靠性的策略的描述性总结.
结论:
- 强调有必要采用可靠的方法,以确保在证据综合中使用的数据的质量和完整性.
- 为寻求解决数据质量挑战的证据合成器提供基础资源.
- 强调主动采取措施的重要性,以防止在科学文献中存在欺诈性或错误数据的影响.
相关概念视频
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01:15Data Validation
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01:22Eyewitness Memory
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01:18False Memories
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01:31Proofreading
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