PubMed · 9710979
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P A Christou. 1998. Demystifying HazCom.. https://pubmed.ncbi.nlm.nih.gov/9710979/
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Performing uncertainty analysis on compartmental models is the main topic of this article. Elements of the methodology developed during a joint CEC/USNRC accident consequence code uncertainty analysis are introduced. The uncertainty is quantified using structured expert judgment. Experts are queried about physically observable quantities. Many code input parameters of the accident consequence codes are not physically observable but are used to predict observable quantities. Therefore, a probabilistic inversion technique was developed which 'transfers' the uncertainty from the physically observable quantities to the code input parameters. The probabilistic inversion technique is illustrated using the compartmental model of systemic retention of Sr in the human body. The article is concluded with a discussion on capturing uncertainty via compartmental models.
BACKGROUND: Health and safety training for hazardous materials workers is among OSHA's major policies. A large and growing workforce in this area, and the resulting risks for these workers and the public, make quality training critical. Measuring trainees' individual knowledge following training is a common but controversial practice. METHODS: Technical issues and benefits in testing, strategies for mitigating the limitations of testing, and the relevance of testing at a broader policy level were examined from the perspective of a large and diverse program. RESULTS: Knowledge data from individuals greatly aided in evaluating program effectiveness at the time of training and in assessing workplace impact later. Use of sound testing principles and creative examination methods and materials, and collaboration across programs, all helped to address concerns for individual programs and the field generally. CONCLUSIONS: Programs would benefit from fully considering the benefits and options related to knowledge assessment in training. Those who choose to assess individual knowledge could move the process forward through added rigor, collaboration, and documentation of efforts.
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