Small business owners' health and safety intentions: A cross-sectional survey

BACKGROUND: Little is known about the variables underlying small business owners' behavioural intentions toward workplace health and safety. This project explores the relationship between three mediating variables (Attitude Toward Safety, Subjective Norm and Perceived Behavioural Control) and owners' Intentions Toward Safety, following the Theory of Planned Behaviour. We also investigate the role of beliefs underlying each mediating variable. METHODS: Seven hundred businesses (5–50 employees) were randomly selected from 4084 eligible companies in a manufacturing business database (SIC codes 24 to 39). The 348 respondents are on average 51 yrs of age, 86% male, 96% white and have 2 to 4 years of post-secondary school. RESULTS: All three mediator variables are significantly correlated with Intentions Toward Safety; Attitude Toward Safety shows the strongest correlation, which is confirmed by path analysis. Owners with higher attitudes toward safety have a higher probability of believing that improving workplace health and safety will make employees' healthier and happier, show that they care, increase employee productivity, lower workers' compensation costs, increase product quality and lower costs. CONCLUSION: These results suggest that interventions aimed at increasing owners' health and safety intentions (and thus, behaviours) should focus on demonstrating positive employee health and product quality outcomes

From Positron to Pattern:A Conceptual and Practical Overview of 18F-FDG PET Imaging and Spatial Covariance Analysis

Imaging of brain glucose metabolism with 18F-2-fluoro-2-deoxy-d-glucose positron emission tomography (18F-FDG PET) can give important information regarding disease-related changes in underlying neuronal systems, when combined with appropriate analytical methods. One such method is the scaled subprofile model combined with principal component analysis (SSM PCA). This model takes into account the relationships (covariance) between voxels to identify disease-related patterns. By quantifying disease-related pattern expression on a scan-by-scan basis, this technique allows objective assessment of disease activity in individual subjects. This chapter provides an overview of steps involved in pattern identification in 18F-FDG PET data and is divided into three sections. Section 1 introduces basic concepts in nuclear imaging and explores the cellular underpinnings of signals measured with 18F-FDG PET. Section 2 describes relevant basic concepts in 18F-FDG PET image analysis including anatomical registration, normalization, and analysis of variance and covariance. Section 3 is dedicated to SSM PCA specifically. The goal of this chapter is to make the technique more accessible to readers without a mathematics or neuroimaging background. Although many excellent texts on this topic exist, the current chapter aims to provide a more conceptual overview, including some discussion points that are not always formally described in literature.</p