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Self-efficacy (SE) is an important determinant for the psychological adjustment of people with spinal cord injury (SCI). However, little is known about the course of SE during inpatient rehabilitation up to 1 year after discharge. The aim of this study was to determine latent trajectory classes of SE, depressive mood, and anxiety in people with SCI, as well as the interrelationships between these trajectories. Longitudinal inception cohort study. Eight specialized SCI rehabilitation centers. The participants (N=268) were mainly men 183 of 268 (68.3%) with a mean age of 55.6 years. Almost half had a traumatic SCI 135 of 268 (50.4%) and tetraplegia (53.7%), and the minority had a motor complete SCI (32.2%). Not applicable. SE was measured using the University of Washington Self-Efficacy Scale. In addition, the Hospital Anxiety and Depression Scale was used to asses distress and perform dual trajectory modeling analyses. Three trajectories of SE, indicating low, middle, and high SE, could be distinguished. Furthermore, a 2-class trajectory solution for depressive mood and a 4-class solution for anxiety were found to be most suitable. All trajectories were stable over time. Developmental connections between SE and depressive mood and between SE and anxiety were revealed. In particular, participants who adjusted well, reporting low scores on depressive mood and anxiety, could be identified by their high SE scores. However, the group of participants with high depressive mood scores and anxiety scores could not always be identified based on their SE trajectory. In accordance with our hypotheses, distinct trajectories of SE, depressive mood, and anxiety were identified and high probabilities that SE trajectories were interrelated to the trajectories from depressive mood and anxiety were confirmed. Concurrent screening for SE and distress might best detect people at risk for adjustment problems. [ABSTRACT FROM AUTHOR]

The article focuses on a study about the Risk Level Based Management System (RLBMS), an occupational risk management (ORM) model related to occupational safety, hygiene, and health (OSHH) resources for hazardous procedures at work. The model categorizes the risk levels (RLs) and characterizes the risks for commonly performed activities. The RLBMS tracks the activities, increases OSHH work time and presents control measures to a RLs for a task.

Evidence of a managerial influence on occupational risk prevention is largely lacking. The incidence of major accidents in high-hazard sectors of industry does not show a downward trend. Also, the decline of fatal occupational accidents in industrialized Western countries may well be attributed to the growing employment in the relatively safe service sectors and to a phenomenon known as export of hazards. Looking more closely at accidents and disasters, we now believe they are not only caused by direct physical events, nor by human errors alone. They have their roots in organizational settings and in the sociotechnical system companies are active in. Whatever their cause, we know that (major) accidents almost always take us by surprise. Despite all our efforts and systems, we seem unable to foresee or predict these events. It seems our management systems are looking at the wrong items. Critics from small- and medium-sized enterprises also point in that direction; management systems are too bureaucratic and lack a focus on hazard and risk identification. Apparently, we fail to incorporate the main ingredients of accident causation in our management systems. This article will discuss current models and presentations. In reference to the title of this article, the possibility to integrate these presentations into management systems will be discussed. This article is based on the presentation given at the 4th International Conference on Occupational Risk Prevention, Sevilla, Spain, May 10–12, 2006. © 2008 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

The paper describes the methods used for a study of the safety management system and culture of a cold rolling mill in a steel plant. It uses data from diverse sources which can be validated against each other and combined to produce a rich picture of the current risk control system and its shortcomings. The paper also describes the proposed improvements and what the plant chose to implement. This step also throws light on the culture of the plant. [ABSTRACT FROM AUTHOR]