质量保证评审:它们与同行评审有何不同。

N H Adams
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引用次数: 3

摘要

美国环境保护署的科学家和工程师撰写的研究论文和报告由该机构的质量保证人员进行审查。环境保护署的文件和报告要接受同行评审,以检查结论的有效性和与主题领域的技术知识体系的总体一致性。质量保证评审与同行评审的不同之处在于,质量保证评审的重点是在以下标准上:一致性:是否在整个过程中使用了合理和一致的度量单位和普遍可接受的公式?是否报告了适当数量的有效数字?正确性:是否使用了矩阵兼容方法?测量是否在该方法的工作范围内?测量结果是否可以追溯到公认的标准或来源(例如,国家标准与技术研究所)?从具有代表性的原始数据开始,到论文或报告中的汇总数据,是否可以验证计算结果?连贯性:所陈述的结论是否符合所提供的数据?假设是否明确说明?是否讨论了数据和结论之间的不一致性?清晰度:是否定义了特殊术语和缩写?具有该学科一般技术背景的人能理解论文或报告吗?一致性:研究是否遵循测试/质量保证计划,是否有适当的校准和其他质量控制检查、审计和数据验证?如果没有,是否有问题的讨论?一致性:是否达到了数据质量目标?数据质量指标是否达到了精度、准确性、代表性、可比性和完整性的目标?讨论了这些质量保证审查标准的重要性以及当前工作中的例子。
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Quality assurance reviews: how they differ from peer reviews.

Research papers and reports written by scientists and engineers in the United States Environmental Protection Agency are reviewed by the agency's quality assurance staff. EPA papers and reports are subjected to peer reviews that check for the validity of conclusions and the general agreement with the body of technical knowledge in the subject area. Quality assurance reviews differ from peer reviews in that the focus of the quality assurance review is on the following criteria: Consistency: Were reasonable and consistent units of measurement and generally acceptable formulas used throughout? Are the appropriate number of significant figures reported? Correctness: Were matrix-compatible methods used? Were measurements within the working range of the method? Can measurements be traced to a recognized standard or source (e.g., the National Institute of Standards and Technology)? Can calculations be verified, starting from representative raw data and proceeding to the summary data presented in the paper or report? Coherence: Do the stated conclusions follow from the data presented? Are the assumptions clearly stated? Are inconsistencies between data and conclusions discussed? Clarity: Are special terms and acronyms defined? Can a person with a general technical background in the subject understand the paper or report? Conformance: Did the study follow the test/quality assurance plan, with appropriate calibrations and other quality-control checks, audits, and data validations? If not, is there a discussion of problems? Concordance: Were data quality objectives met? Were the data quality indicator goals achieved for precision, accuracy, representativeness, comparability, and completeness? The importance of these quality assurance review criteria are discussed along with examples from current work.

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