Risk factors for digital stress in German public administrations

Objective As the digitization of the working world progresses, the demands on employees change. Not least, this is true for the setting of public administrations in Germany, which is currently affected by the transformation to E-Government. This study aims to identify and describe a risk cluster of digitally stressed employees in public administrations. Methods An online sample of 710 employees from three public administrations in North Rhine-Westphalia were surveyed about digital stress (7 items) and several potential risk factors (19 items) derived from the current research. In the first step, a hierarchical agglomerative cluster analysis is used to detect the risk cluster. This is followed by a comparison to the group of the remaining employees regarding their risk profiles. Results The analysis states that the digitally stressed cluster accounts for approximately ten percent of the public administration’s employees of the total sample. Employees in the risk cluster are less satisfied with on-site work overall, experience less collegial support on-site, experience less collegial support in the home office, resign more often, are more likely to feel overwhelmed, are less educated, are older in age and more often have relatives in need of care. Conclusion This work was able to identify and describe a group of digitally stressed rather than left-behind employees in public administrations to bring awareness to potentially destructive factors in the digital transformation process but eventually to social inequalities. The findings offer the basis for interventions to arise and evoke potential for further research.

Risk Factors for Digital Stress in German Public Administrations

Objective: While the digitization of the working world progresses, the demands on employees are changing. Not least this is true for the setting of public administrations in Germany which is currently affected by the transformation to E-Government. This study aims at identifying and describing a risk cluster of digitally stressed employees in those public administrations. Methods: An online sample of 710 employees from three public administrations in North Rhine-Westphalia was asked about digital stress (7 items) and several potential risk factors (19 items) which were derived from current research. In a first step, a hierarchical agglomerative cluster analysis is used to detect the risk cluster. This is followed by a comparison to the group of the remaining employees regarding their risk profiles. Results: The analysis states, that the cluster of digitally stressed takes around ten percent of the public administration’s employees of the total sample. Employees in the risk cluster are less satisfied with on-site work overall, experience less collegial support on-site, experience less collegial support in the home office, resign more often, are more likely to feel overwhelmed, are less educated, are older in age and more often have relatives in need of care.Conclusion: This work managed to identify and describe a group of digitally stressed, rather than left-behind employees in public administrations to bring awareness to potentially destructive factors in the digital transformation process, but eventually to social inequalities as well. The findings offer the basis for interventions to arise as well as it evokes potential for further research.

Pain-Associated Clusters Among Nursing Home Residents

Chronic pain is common in older adults, particularly among nursing home residents (NHR). Internationally, the reported pain prevalence among NHR ranges from 3.7% to 79.5%. At least one in two German NHR are diagnosed with pain. Unrelieved chronic pain is associated with reduced physical functioning and psychological parameters. Given the high prevalence of pain among NHR, we hypothesized that there were likely pain-associated clusters in our target group. Clustering is an opportunity to identify differences in pain management and may enable better targeted health-service delivery for professionals. There are no available data regarding pain-associated clusters (sub-groups of NHR) based on different items measuring pain. This study was performed using baseline data, and was part of a cluster-randomized controlled trial conducted in 12 nursing homes in Berlin. We assessed pain using the German Brief Pain Inventory (BPI-NHR) among 137 NHR (mean age, 83.33 years) capable of self-report. We performed hierarchical agglomerative cluster analysis to generate three clusters (naming is based on the mean value of each BPI-NHR item in each cluster): pain-relieved (46.72 %), pain-restricted (22.63 %), and severe pain (30.66 %). Body-Mass-Index (F(2,129) = 4.274, P = 0.016), Barthel-Index (F(2,133) = 3.246, P = 0.042), and appropriateness of pain medication (F(2,119) = 12.007, P = 0.000) differed between clusters. Parameters associated with an increased or decreased risk of being in a pain-diagnosed cluster will be discussed. The observed need for clinical interventions aiming at shifting from pain-diagnosed clusters to pain-relieved status will be reflected.

Post-graduation Plans of Undergraduate BME Students: Gender, Self-efficacy, Value, and Identity Beliefs

This study investigates career intentions and students’ engineering attitudes in BME, with a focus on gender differences. Data from n = 716 undergraduate biomedical engineering students at a large public research institution in the United States were analyzed using hierarchical agglomerative cluster analysis. Results revealed five clusters of intended post-graduation plans: Engineering Job and Graduate School, Any Job, Non-Engineering Job and Graduate School, Any Option, and Any Graduate School. Women were evenly distributed across clusters; there was no evidence of gendered career preferences. The main findings in regard to engineering attitudes reveal significant differences by cluster in interest, attainment value, utility value, and professional identity, but not in academic self-efficacy. Yet, within clusters the only gender differences were women’s lower engineering academic self-efficacy, interest and professional identity compared to men. Implications and areas of future research are discussed.

The pathways and conditions of effectively combating the COVID-19 pandemic in Asia

The rapid outbreak of COVID-19 pandemic urges to seek advanced solutions to combat the disease and minimize its impacts on humankind and society. We employ a causal mechanism approach and develop a crisp-set Qualitative Comparative Analysis (QCA) model to study successful pathways in curbing COVID-19 among 37 Asian countries. Structural variables of GDP per capita, governance, democracy, health access and quality alongside with two government response indexes from the real-time Oxford COVID-19 Government Response Tracker are included as causal conditions in our QCA model. We identified a total of seven successful pathways, covering countries in different development stages. Regardless of income levels, we found democracy is essential in effectively controlling the pandemic. High democratic scores are characterized in five over seven pathways. Extensive testing and comprehensive contact tracing strategies have proved to be effective in containing COVID-19, especially in developed and emerging countries. Surprisingly, we found limited impacts of stringent containment measures such as gathering restricted and social distancing. We traced back to the early days of COVID-19, and by performing hierarchical agglomerative cluster analysis, we learned that restrictive containment measures in the early days helped prevent the spread of the pandemic, especially in developing countries. In the context that democracy is in decline around the world, our empirical results suggest that democracy is still essential in controlling the disease. Also, countries in under-resourced settings can still effectively combat COVID-19 with appropriate and timely containment measures.

Early behavioral profiles elucidating vulnerability and resiliency to later ASD outcomes

Infant siblings of children with autism spectrum disorder (ASD) exhibit greater heterogeneity in behavioral presentation and outcomes relative to infants at low familial risk (LR), yet there is limited understanding of the diverse developmental profiles that characterize these infants. We applied a hierarchical agglomerative cluster analysis approach to parse developmental heterogeneity in 420 toddlers with heightened (HR) and low (LR) familial risk for ASD using measures of four dimensions of development: language, social, play, and restricted and repetitive behaviors (RRB). Results revealed a two-cluster solution. Comparisons of clusters revealed significantly lower language, social, and play performance, and higher levels of restricted and repetitive behaviors in Cluster 1 relative to Cluster 2. In Cluster 1, 25% of children were later diagnosed with ASD compared to 8% in Cluster 2. Comparisons within Cluster 1 between subgroups of toddlers having ASD+ versus ASD− 36-month outcomes revealed significantly lower functioning in the ASD+ subgroup across cognitive, motor, social, language, symbolic, and speech dimensions. Findings suggest profiles of early development associated with resiliency and vulnerability to later ASD diagnosis, with multidimensional developmental lags signaling vulnerability to ASD diagnosis.

Towards a dynamic behavioral profile: A diachronic study of polysemous sentir in Spanish

This study examines the diachronic evolution of the polysemy of the Spanish verbsentir(‘to feel’) by means of a corpus-based dynamic behavioral profile (BP) analysis. Methodologically, it presents the first application of the BP approach to historical data and proposes some methodological innovations not only within the current body of research in historical semantics but also with regard to previous applications of the BP approach. First, whereas the majority of existing studies in quantitative historical semantics are largely based on observed frequencies or percentages of collocational co-occurrence, our study leverages more complex historical data that are based on the similarities of vectors. Second, this study also provides an extension of the methodological apparatus of the BP approach by complementing the traditional hierarchical agglomerative cluster analysis (HAC) with a dynamic BP approach derived from multidimensional scaling maps (MDS). Theoretically, this methodology contributes to a comprehensive perspective on the process of Constructionalization and the nature of networks, which is illustrated on the basis of the development of the Discourse Marker (DM)lo siento(‘I’m sorry’).

Cluster Analysis of Haze Episodes Based on Topological Features

Severe haze episodes have periodically occurred in Southeast Asia, specifically taunting Malaysia with adverse effects. A technique called cluster analysis was used to analyze these occurrences. Traditional cluster analysis, in particular, hierarchical agglomerative cluster analysis (HACA), was applied directly to data sets. The data sets may contain hidden patterns that can be explored. In this paper, this underlying information was captured via persistent homology, a topological data analysis (TDA) tool, which extracts topological features including components, holes, and cavities in the data sets. In particular, an improved version of HACA was proposed by combining HACA and persistent homology. Additionally, a comparative study between traditional HACA and improved HACA was done using particulate matter data, which was the major pollutant found during haze episodes by the Klang, Petaling Jaya, and Shah Alam air quality monitoring stations. The effectiveness of these two clustering approaches was evaluated based on their ability to cluster the months according to the haze condition. The results showed that clustering based on topological features via the improved HACA approach was able to correctly group the months with severe haze compared to clustering them without such features, and these results were consistent for all three locations.

On the polysemy of the Polish complete path construction: A corpus-based exploratory study

In this article, we present a corpus-based analysis of two major types of the Polish Complete Path (CP) construction in which a source-PP, headed by od+GEN, is immediately followed by a goal-PP, headed by do+GEN or po+ACC, as in od jesieni 1920 do jesieni 1921 ‘from autumn 1920 to autumn 1921’ and od kreskówek po rysunki techniczne ‘from cartoons to technical drawings’. The aim of the study is to shed some light on the polysemous structure of the CP construction on the basis of its usage patterns. To this end, we used a random sample of over 500 instances of both construction types retrieved from the National Corpus of Polish. The data were annotated for a number formal and semantic features and subsequently explored using hierarchical agglomerative cluster analysis. When interpreting the results of several analyses performed on different sets of variables, we gave special attention to three levels of semantic granularity encoded in the data, concluding that, on the whole, all analyses point towards a distinction between the spatial, temporal and abstract meanings of the construction under investigation.