The present study focuses on the level of stress a teacher perceives when dealing with the most behaviorally challenging student in his or her classroom. To measure stress in Dutch elementary classrooms, a sample was drawn of 582 teachers. Two questions concerning this relation between student and teacher will be addressed. First of all, we focus on background variables of teachers and students as sources of variation in explaining the magnitude of challenging student behavior and the associated level of stress teachers experience. The second topic of this paper is to accommodate the potentially stressful relationship between student and teacher in a wider network of surrounding variables, which are, Self-efficacy, Negative affect, Autonomy in taking decisions, and Support amongst colleagues. To evaluate the presence of challenging behavior, the behavior of the student is related to more general variables like student responsibility, class size and ratio of boys to girls. We close our paper by assessing the validity of the studied relationship between teacher and student with respect to possible burnout.
The demanding environment that contemporary dance students are exposed to could result in high stress levels, which can influence injury susceptibility. Therefore, this study aims to investigate the association between stress and injuries. In the period between September 2016 and March 2020, four cohorts of first-year dance students (N = 186; mean age 19.21 ± 1.35 years) were followed for one academic year. Each month, general stress was assessed on a 0-100 visual analogous scale. The Oslo Sports Trauma Research Center Questionnaire on Health Problems was used on a monthly basis to monitor injuries. Injuries were defined as "all injuries" (i.e., any physical complaint irrespective of the need for medical attention or time-loss from dance) and "substantial injuries" (i.e., leading to moderate/severe/complete reductions in training volume or performance). Mann-Whitney tests were performed to measure differences in general stress levels between injured and injury-free students, while repeated-measures ANOVA were performed to investigate whether general stress scores increased before and during injury occurrence. The overall average monthly general stress score over all cohorts for all students was 39.81. The monthly general stress scores ranged from 31.75 to 49.16. Overall, injured and substantially injured students reported higher stress scores than injury-free students, with significant differences in 3 out of the 9 months for all injuries (September, October, March, p < 0.05), and in 5 months for substantial injuries (September, October, November, December, April, p < 0.05). Within the 3-month period before and during injury occurrence, a (marginally) significant linear effect of general stress across the time periods was found for all injuries [F(1.87,216.49) = 3.10, p = 0.051] and substantial injuries [F(2,138) = 4.16, p = 0.018]. The results indicate an association between general stress and injuries. Future research should focus on effects of varying stress levels on injury risk using higher sampling frequency, for instance by measuring weekly since stress levels are likely to fluctuate daily. Practically, strategies aiming at stress reduction might have the potential to reduce the burden of dance injuries and may have positive outcomes for dancers, teachers, schools, and companies.
Recent studies show that students increasingly suffer from psychological complaints, including a high degree of (study) stress. If stress persists for a long time, it can have negative consequences for your health and can lead to a burnout, for example. A possible buffer against stress and a positive counterpart of a burnout is engagement. This infographic contains the most important results of a study into stress among students.
Due to the existing pressure for a more rational use of the water, many public managers and industries have to re-think/adapt their processes towards a more circular approach. Such pressure is even more critical in the Rio Doce region, Minas Gerais, due to the large environmental accident occurred in 2015. Cenibra (pulp mill) is an example of such industries due to the fact that it is situated in the river basin and that it has a water demanding process. The current proposal is meant as an academic and engineering study to propose possible solutions to decrease the total water consumption of the mill and, thus, decrease the total stress on the Rio Doce basin. The work will be divided in three working packages, namely: (i) evaluation (modelling) of the mill process and water balance (ii) application and operation of a pilot scale wastewater treatment plant (iii) analysis of the impacts caused by the improvement of the process. The second work package will also be conducted (in parallel) with a lab scale setup in The Netherlands to allow fast adjustments and broaden evaluation of the setup/process performance. The actions will focus on reducing the mill total water consumption in 20%.
Motivatie Het versterken van de samenwerking tussen relevante lectoraten door het ontwikkelen van een multidisciplinaire onderzoeksagenda op het terrein van Arbeid in de brede zin van het woord. Hierdoor kan de thematiek rondom toegang tot en behoud van arbeid vanuit meerdere kanten worden aangevlogen én kan focus en massa worden gecreëerd voor onderzoeksprogrammering en –funding. Daardoor kunnen we als lectoraten een belangrijke rol te spelen bij vraagstukken die betrekking hebben op het duurzaam (weer) aan het werk gaan én duurzaam aan het werk blijven. Achtergrond Om als individu zelfstandig en volwaardig te kunnen deelnemen aan onze participatiemaatschappij, is het hebben van werk cruciaal. Werk is echter voor mensen met minder of onvoldoende arbeids-, persoonlijk-, sociaal-, en cultureel kapitaal en/of toegang tot hulpbronnen steeds minder vanzelfsprekend. Naast traditioneel kwetsbare groepen – zoals laagopgeleiden, mensen met een chronische aandoening en migranten - zijn er nieuwe categorieën, waaronder veel middelbaar en hoog opgeleiden, voor wie het lastig is/wordt structureel betaald werk te vinden. De oorzaak ligt voornamelijk bij de toenemende digitalisering en robotisering in combinatie met de flexibilisering van de arbeidsmarkt. Ook werk op academisch niveau, dat gebaseerd is op regels, bijvoorbeeld accountancy en rechtspraak, zal steeds vaker (deels) geautomatiseerd kunnen worden (Est et al. 2015, Went et al. 2015). Anderzijds zijn er sectoren, zoals techniek en ICT, die een steeds grotere behoefte hebben aan hoogopgeleid personeel en waar het lastig is om voldoende gekwalificeerde mensen te krijgen. Tot slot zien we in alle sectoren een toename van stress- en burn-out klachten, die deels gerelateerd zijn aan traditionele, functioneel ingerichte organisaties. Het bovenstaande biedt geen rooskleurig beeld voor grote groepen in de samenleving en vanuit een breed Platform Arbeid willen we de thema’s op het terrein van arbeid vanuit meerdere perspectieven benaderen en in samenhang beschouwen.
Production processes can be made ‘smarter’ by exploiting the data streams that are generated by the machines that are used in production. In particular these data streams can be mined to build a model of the production process as it was really executed – as opposed to how it was envisioned. This model can subsequently be analyzed and stress-tested to explore possible causes of production prob-lems and to analyze what-if scenarios, without disrupting the production process itself. It has been shown that such models can successfully be used to diagnose possible causes of production problems, including scrap products and machine defects. Ideally, they can even be used to model and analyze production processes that have not been implemented yet, based on data from existing production pro-cesses and techniques from artificial intelligence that can predict how the new process is likely to be-have in practice in terms of data that its machines generate. This is especially important in mass cus-tomization processes, where the process to create each product may be unique, and can only feasibly be tested using model- and data-driven techniques like the one proposed in this project. Against this background, the goal of this project is to develop a method and toolkit for mining, mod-elling and analyzing production processes, using the time series data that is generated by machines, to: (i) analyze the performance of an existing production process; (ii) diagnose causes of production prob-lems; and (iii) certify that a new – not yet implemented – production process leads to high-quality products. The method is developed by researching and combining techniques from the area of Artificial Intelli-gence with techniques from Operations Research. In particular, it uses: process mining to relate time series data to production processes; queueing networks to determine likely paths through the produc-tion processes and detect anomalies that may be the cause of production problems; and generative adversarial networks to generate likely future production scenarios and sample scenarios of production problems for diagnostic purposes. The techniques will be evaluated and adapted in implementations at the partners from industry, using a design science approach. In particular, implementations of the method are made for: explaining production problems; explaining machine defects; and certifying the correct operation of new production processes.
