How do we Understand Working Environment Policies, Programmes and Instruments?

How do different forms of regulation influence working environment and working life? How can centrally formulated programmes create changes at complex, often multi-layered local work places, and how can insights from work life studies shed light on the social mechanisms at stake in different types of regulation? Under the heading of working environment policies, programmes and instruments, the aim of the current issue has been to address these issues. In essences, it is society’s intentional attempt to regulate working environment conditions in the workplace. It could be through health and safety legislation and labour inspection. But it is much more than that: The states are not restricted to writing rules, inspecting and sanctioning them. They put together insurances systems and they support massive campaign efforts. And they get labour market parties, nationally, by the sector or within the companies or workplaces, involved in similar activities; sometimes, labour market parties or other stakeholders even do it without prompts from the state. All these efforts are made to a larger or smaller extent because they are seen as beneficial to the health of the employees. But we know surprisingly little about how policies, programmes and instruments work, the social processes at stake, to what extent they do work and whether there could be better ways to reach the overall goal of creating a better working environment. There seems to be several reasons for the lack of knowledge about the processes involved in regulating the working environment (...

Applying Chemometrics to Evaluate Mine Tailings’ Potential As Partial Cement Replacement

This study investigates the utilization of mine tailings, the by-product originating from metal- and mineral-based ore mining, as a new cement replacement material. This paper is based on the chemical and physical characteristics of 13 mine tailing samples. In this study, Chemometrics were applied to consider all parameters simultaneously and obtain a thorough screening of potential relations in the large data set. Hierarchical Cluster Analysis (HCA) groups samples according to (dis)similar features and Principal Component Analysis (PCA) visualizes predominating variables and relations to samples. The application of HCA highlighted a clear grouping between mine tailings according to characteristics. Meanwhile, PCA identified the predominant chemical and physical characteristics in the mine tailing samples. Chemometrics therefore provided a thorough overview of mine tailings’ physical and chemical characteristics. Keywords: mine tailings, chemometrics, cement replacemen

Self-rated health, ethnicity and social position in a deprived neighbourhood in Denmark

<p>Abstract</p> <p>Background</p> <p>In recent years the close connection between SES and differences in health between ethnic groups have been subject to growing interest among researchers, and some studies have found an association between ethnicity and long term illness and poor health. However, there is limited research-based knowledge about health and illness in ethnic groups in Denmark and about ethnic Danes living in deprived neighbourhoods. The purpose of this study is to investigate associations between self-rated health and ethnicity and social position in a deprived neighbourhood in Denmark in which a relatively largely proportion of the residents are immigrants.</p> <p>Methods</p> <p>This study investigates the association between self-rated health used as dependent variable and ethnicity and social position (defined as index for life resources) as the independent variables. The analyses are based on data collected in a survey in a geographically bounded and social deprived neighbourhood, Korskaerparken, located in the municipality of Fredericia in Denmark. The sample consisted of 31% of the residents in Korskaerparken and of these 29% have an ethnic background other than Danish.</p> <p>The analyses were conducted using logistic regression adjusting for confounding variables.</p> <p>Results</p> <p>This study indicates no significant association between ethnicity and having poor/very poor self-rated health.</p> <p>On the other hand the study confirms that a strong and significant association between the number of residents' life resources and their self-rated health does indeed exist. The results clearly suggest that the more life resources an individual has, the lower is the risk of that individual reporting poor health.</p> <p>Conclusion</p> <p>The results show a strong association between the residents' number of life resources and their self-rated health. In this study, we were not able to identify any association between ethnicity and self-rated health, i.e. our results suggest that ethnicity does not constitute an explanation to differences in self- rated health.</p