Integrating Behavioral Science with Human Factors to Address Process Safety

ABSTRACT

Applying the science of human factors to eliminate error across all aspects of process design, management, operation, and maintenance has been a focus in the process safety area for many years. Human error has been attributed as a major cause of many high profile catastrophic accidents around the world. These accidents have resulted in national and international attention, which has led to a focus on improving organizational capabilities, systems, and in many cases, governmental regulations around human factors. This article provides a review of the field of human factors highlighting various topics in the literature, and introduces governmental regulatory bodies currently engaging organizations in a scientific approach to human factors. Finally, the need for integrating behavioral science methodologies with human factors is addressed. This is done with specific focus on how Organizational Behavior Management methodologies can work in concert with human factors to optimize process safety.     

A method to investigate drivers' acceptance of Blind Spot Detection System®

Lately, with the goal of improving road safety, car makers developed and commercialised some Advanced Driver Assistance Systems (ADAS) which, through the detection of blind spot areas on the vehicle's sides, could help the drivers during the overtaking and the change lane task. Despite the possible benefits to reduce lateral crashes, the overall impact on road safety of such systems have not been deeply studied yet; notably, despite some researches have been carried out, there is a lack of studies regarding the long-term usage and drivers' acceptance of those systems. In order to fill the research gap, a methodology, based on the combination of focus groups interviews, questionnaires and a small-scale field operational test (FOT), has been designed in this study; such a methodology aims at evaluating drivers' acceptance of Blind Spot Information System? and at proposing some ideas to improve the usability and user-friendliness of this (or similar) device in their future development.     

Social Policy and Development in Latin America: The Long View

In the new development strategy currently shaping Latin America, alternative social policy models have emerged. This article argues that far from being rival alternatives, each of these models considers the wide differences among countries. The region is emerging from a century of transformation – from a traditional agrarian economy to an urban industrial one – in which countries have taken diverse historical paths. Some have almost completed this transformation, others are taking early steps, and the vast majority are living through it. State‐led transition has followed two successive development strategies. From the 1920s up to the 1980s, state developmentalism has mostly successfully assumed the twin challenges of economic and social progress. In the last two decades of the century, Latin American states adopted the policies of the Washington Consensus, which emphasized the importance of business in the framework of globalization and benefited the affluent few. However, an unambiguous shift in direction has been taking place in Latin America since the 1997 economic crisis. This article suggests that a new developmental welfare state model seems to be in the making. How will it evolve over the wider space of an increasingly integrated Latin America?     

Landscape Context Influences the Abundance and Richness of Native Lady Beetles Occupying Urban Vacant Land

Urban Ecosystems - Urban insect communities are structured by hierarchical filters including regional processes, human facilitation, and species interactions. We sought to inform future urban...     

Removing barriers to technology

Health care executives need to take every step possible to overcome barriers to adoption of new technology by physicians. Through just a few relatively simple and inexpensive strategies, they can enable their physicians to use the clinical information system and bring value to their patients, health system and medical staff.     

On influence diagnostic in univariate elliptical linear regression models

We discuss in this paper the assessment of local influence in univariate elliptical linear regression models. This class includes all symmetric continuous distributions, such as normal, Student-t, Pearson VII, exponential power and logistic, among others. We derive the appropriate matrices for assessing the local influence on the parameter estimates and on predictions by considering as influence measures the likelihood displacement and a distance based on the Pearson residual. Two examples with real data are given for illustration.     

A multivariate version of Gini's rank association coefficient

In this paper, we introduce a multivariate generalization of the population version of Gini's rank association coefficient, giving a response to this open question posed in [4]. We also study some properties of this version, present the corresponding results for the sample statistic, and provide several examples.     

A Statistical Framework for Hypothesis Testing in Real Data Comparison Studies

In computational sciences, including computational statistics, machine learning, and bioinformatics, it is often claimed in articles presenting new supervised learning methods that the new method performs better than existing methods on real data, for instance in terms of error rate. However, these claims are often not based on proper statistical tests and, even if such tests are performed, the tested hypothesis is not clearly defined and poor attention is devoted to the Type I and Type II errors. In the present article, we aim to fill this gap by providing a proper statistical framework for hypothesis tests that compare the performances of supervised learning methods based on several real datasets with unknown underlying distributions. After giving a statistical interpretation of ad hoc tests commonly performed by computational researchers, we devote special attention to power issues and outline a simple method of determining the number of datasets to be included in a comparison study to reach an adequate power. These methods are illustrated through three comparison studies from the literature and an exemplary benchmarking study using gene expression microarray data. All our results can be reproduced using R codes and datasets available from the companion website http://www.ibe.med.uni-muenchen.de/organisation/mitarbeiter/020_professuren/boulesteix/compstud2013.