Manoeuvring between anxiety and control : Patients experience of learning to live with diabetes: a lifeworld phenomenological study

Research shows that people with diabetes want their lives to proceed as normally as possible, but some patients experience difficulty in reaching their desired goals with treatment. The learning process is a complex phenomenon interwoven into every facet of life. Patients and healthcare providers often have different perspectives in care which gives different expectations on what the patients need to learn and cope with. The aim of this study, therefore, is to describe the experience of learning to live with diabetes. Interviews were conducted with 12 patients afflicted with type 1 or type 2 diabetes. The interviews were then analysed with reference to the reflective lifeworld research approach. The analysis shows that when the afflicted realize that their bodies undergo changes and that blood sugar levels are not always balanced as earlier in life, they can adjust to their new conditions early. The afflicted must take responsibility for balancing their blood sugar levels and incorporating the illness into their lives. Achieving such goals necessitates knowledge. The search for knowledge and sensitivity to changes are constant requirements for people with diabetes. Learning is driven by the tension caused by the need for and dependence on safe blood sugar control, the fear of losing such control, and the fear of future complications. The most important responsibilities for these patients are aspiring to understand their bodies as lived bodies, ensuring safety and security, and acquiring the knowledge essential to making conscious choices.

Role of headmasters, teachers and supervisors in knowledge transfer about occupational health and safety to pupils in vocational education

Background Young people are at an increased risk for illness in working life. The authorities stipulate certain goals for training in occupational health and safety (OHS) in vocational schools. A previous study concluded that pupils in vocational education had limited knowledge in the prevention of health risks at work. The aim of the current study, therefore, was to study how OHS training is organized in school and in workplace-based learning (WPL).   Method The study design featured a qualitative approach, which included interviews with 12 headmasters, 20 teachers, and 20 supervisors at companies in which the pupils had their WPL. The study was conducted at 10 upper secondary schools, located in Central Sweden, that were graduating pupils in four vocational programs.   Result The interviews with headmasters, teachers, and supervisors indicate a staggered picture of how pupils are prepared for safe work. The headmasters generally give teachers the responsibility for how goals should be reached. Teaching is very much based on risk factors that are present in the workshops and on teachers’ own experiences and knowledge. The teaching during WPL also lacks the systematic training in OHS as well as in the traditional classroom environment.   Conclusion Teachers and supervisors did not plan the training in OHS in accordance with the provisions of systematic work environment management. Instead, the teachers based the training on their own experiences. Most of the supervisors did not get information from the schools as to what should be included when introducing OHS issues in WPL.

Machine Learning Algorithms in Heavy Process Manufacturing

In a global economy, manufacturers mainly compete with cost efficiency of production, as the price of raw materials are similar worldwide. Heavy industry has two big issues to deal with. On the one hand there is lots of data which needs to be analyzed in an effective manner, and on the other hand making big improvements via investments in cooperate structure or new machinery is neither economically nor physically viable. Machine learning offers a promising way for manufacturers to address both these problems as they are in an excellent position to employ learning techniques with their massive resource of historical production data. However, choosing modelling a strategy in this setting is far from trivial and this is the objective of this article. The article investigates characteristics of the most popular classifiers used in industry today. Support Vector Machines, Multilayer Perceptron, Decision Trees, Random Forests, and the meta-algorithms Bagging and Boosting are mainly investigated in this work. Lessons from real-world implementations of these learners are also provided together with future directions when different learners are expected to perform well. The importance of feature selection and relevant selection methods in an industrial setting are further investigated. Performance metrics have also been discussed for the sake of completion.