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Why Nuclear Incidents Happen
Published in Jonathan K. Corrado, Technology, Human Performance, and Nuclear Facilities, 2023
Latent human error comes into play when individuals’ propensity for error is enhanced by the environment in which they work and the systems with which they interact. Two adverse effects can result from latent conditions: their ability to provoke errors, and their impact on the long-term health and welfare of the system that created them. These conditions do not necessarily contribute immediately to the possibility for error; rather, they can rest hidden within a system until the requisite elements align and cause the latent error to become activated [2].
The contribution of latent human failures to the breakdown of complex systems
Published in R. Key Dismukes, Human Error in Aviation, 2017
It is suggested that latent failures are analogous to the 'resident pathogens' within the human body, which combine with external factors (stress, toxic agencies, etc.) to bring about disease. Like cancers and cardiovascular disorders, accidents in complex, defended systems do not arise from single causes. They occur through the unforeseen (and often unforeseeable) concatenation of several distinct factors, each one necessary but singly insufficient to cause the catastrophic breakdown. This view leads to a number of general assumptions about accident causation.
What Is Marketing?
Published in Olivier Mesly, Marketing Projects, 2020
Needs come in two forms: real or latent (hidden). Real needs are existing needs currently fulfilled by products, services, messages, or experiences supplied by the market. Latent (or hidden) needs are those that are yet unknown, dormant, unexploited, or in the process of being consciously apprehended. Project managers conceive projects, by definition, to respond to latent needs.
An equipment qualification framework for healthcare
Published in IISE Transactions on Healthcare Systems Engineering, 2020
Dermot Hale, Enda F. Fallon, Christine FitzGerald
Adverse events, however, continue to occur in the use of medical equipment. The Institute of Mechanical Engineers (IME) report that, in 2013 in the UK, 13,642 incidents related to faulty medical equipment were reported to the Medicines and Healthcare products Regulatory Agency (MHRA); leading to 309 deaths and 4,955 people sustaining serious injury (IME, 2015). While the immediate causes of adverse events involving medical equipment are typically described as related to either “failure of the equipment” or “use error,” Amoore and Ingram (2002) argue that adverse incidents involving medical equipment are typically multifactorial in origin, with latent factors, faults, errors, and mistakes aligning together. Latent factors, in particular, have been identified as root causes of adverse events (Flewwelling, Easty, Vicente, & Cafazzo, 2014; Karl & Karl, 2012, Van Beuzekom, Boer, Akerboom, & Hudson 2010). It is evident, therefore, that the identification of latent risk factors, prior to the clinical use of medical equipment, can mitigate the risk of adverse events occurring.
Significance of attitudes, passion and cultural factors in driver’s speeding behavior in Oman: application of theory of planned behavior
Published in International Journal of Injury Control and Safety Promotion, 2020
Muhammad Ashraf Javid, Amani Rashid Al-Hashimi
In this research, a structural model was developed for the driver’s SB using SEM technique. Initially, factor analyses were conducted on the driver’s responses. Factor analysis is a technique for identifying groups of variables underlying a set of measures or indicators. Those extracted variables are called factors or latent variables. A latent variable is variable that cannot be directly measured but it is assumed to be related to several observed variables. There are two basic types of factor analysis: Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). EFA is designed for the situation where links between the observed and latent variables are unknown or uncertain. The analysis thus proceeds in an exploratory mode to determine how, and to what extent, the observed variables are linked to their underlying factors. CFA is appropriate to use when the researcher has some knowledge of the underlying latent variable structure. Based on the knowledge of theory and empirical research, researchers postulate relations between the observed measures and the underlying factors and then tests this hypothesized structure statistically. In this paper, the CFA was used for the variables designed considering the background of TPB, whereas the EFA was used for remaining observed data on driving attitudes and personality traits. The SEM is a multivariate statistical analysis technique that is used to analyze structural relationships between underlying observed variables and latent variables. It is the combination of factor analysis and multiple regression analysis. This method is preferred by the researcher because it estimates the multiple and interrelated dependence in a single analysis. It allows the researcher to include a number of unobserved variables in the model and group them in specific factors. The reliability of the developed structural model is assessed using indices of the goodness of fit parameters. Some of these parameters used in this research include the ratio of chi-square to the degree of freedom (chi-sq./DF), Goodness of Fit Index (GFI), Adjusted Goodness of Fit Index (AGFI) and Root Mean Square Error Adjusted (RMSEA) (Byrne, 2001).