Evangelische Hochschule Nürnberg
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- Stress (3)
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This work strives to develop a typological classification of the use of conscious and unconscious defense and coping mechanisms based on methodically and structurally collected data from a qualitative survey of 43 former soldiers in Germany. Seven coping and defense types were identified: the Fighter, the Comrade, the Corpsman, the Strategist, the Partisan, the Self-Protector and the Infantryman. The types identified differed with regard to the accumulation, combination, and use of their conscious and unconscious defense and coping mechanisms in the superordinate areas of behaviour, relationships, emotions, reflexivity and time focus. The typological classification could offer psychotherapeutic interventions tailored to individuals and their defense and coping mechanisms, which could lead to improved therapy use and compliance.
This paper takes up ongoing discussions on the inequality of educational opportunities and formulates a conceptual model to link separate lines of research. Our particular focus is on combining motivational and structural approaches into a mediation model that explains differences in academic achievement. In the literature, four main mechanisms of social reproduction are discussed. Two main pathways refer to (1) parents’ expectations regarding their children’s academic success and (2) replicating cultural capital through intra‐familial cultural practices. (3) Parents’ perception of children’s abilities depends on social position and is influential for expectations of success. (4) For all three pathways, we expect effects on students’ motivational characteristics, which in turn influence academic achievement. We test our conceptual model by structural equation modelling using longitudinal data from primary school students in Germany. Empirical evidence is in line with the assumptions in the model. Cultural reproduction and expectations of success can be seen as the key components of the model. However, both chains of reproduction are related to each other by parents’ perception of child’s ability, and their effects are mediated by child’s motivational characteristics.
Mobile apps are increasingly utilized to gather data for various healthcare aspects. Furthermore, mobile apps are used to administer interventions (e.g., breathing exercises) to individuals. In this context, mobile crowdsensing constitutes a technology, which is used to gather valuable medical data based on the power of the crowd and the offered computational capabilities of mobile devices. Notably, collecting data with mobile crowdsensing solutions has several advantages compared to traditional assessment methods when gathering data over time. For example, data is gathered with high ecological validity, since smartphones can be unobtrusively used in everyday life. Existing approaches have shown that based on these advantages new medical insights, for example, for the tinnitus disease, can be revealed. In the work at hand, data of a developed mHealth crowdsensing platform that assesses the stress level and fluctuations of the platform users in daily life was investigated. More specifically, data of 1797 daily measurements on GPS and stress-related data in 77 users were analyzed. Using this data source, machine learning algorithms have been applied with the goal to predict stress-related parameters based on the GPS data of the platform users. Results show that predictions become possible that (1) enable meaningful interpretations as well as (2) indicate the directions for further investigations. In essence, the findings revealed first insights into the stress situation of individuals over time in order to improve their quality of life. Altogether, the work at hand shows that mobile crowdsensing can be valuably utilized in the context of stress on one hand. On the other, machine learning algorithms are able to utilize geospatial data of stress measurements that was gathered by a crowdsensing platform with the goal to improve the quality of life of its participating crowd users.
Mobile apps are increasingly utilized to gather data for various healthcare aspects. Furthermore, mobile apps are used to administer interventions (e.g., breathing exercises)to individuals. In this context, mobile crowdsensing constitutes a technology, which is used to gather valuable medical databased on the power of the crowd and the offered computationalcapabilities of mobile devices. Notably, collecting data withmobile crowdsensing solutions has several advantages comparedto traditional assessment methods when gathering data overtime. For example, data is gathered with high ecological validity, since smartphones can be unobtrusively used in everyday life. Existing approaches have shown that based on these advantages new medical insights, for example, for the tinnitus disease, can be revealed. In the work at hand, data of a developed mHealth crowdsensing platform that assesses the stress level and fluctuations of the platform users in daily life was investigated. More specifically, data of 1797 daily measurements on GPS and stress-related data in 77 users were analyzed. Using this data source, machine learning algorithms have been applied with the goalto predict stress-related parameters based on the GPS data of the platform users. Results show that predictions become possible that (1) enable meaningful interpretations as well as (2) indicate the directions for further investigations. In essence, the findings revealed first insights into the stress situation of individuals over time in order to improve their quality of life. Altogether, the work at hand shows that mobile crowdsensing can be valuably utilized in the context of stress on one hand. On the other, machine learning algorithms are able to utilize geospatial data of stress measurements that was gathered by a crowdsensing platform with the goal to improve the quality of life of its participating crowd users.
A longitudinal pilot study on stress-levels in the crowdsensing mHealth platform TrackYourStress
(2019)
Background: The mobile phone app, TrackYourStress (TYS), is a new crowdsensing mobile health platform for ecological momentary assessments of perceived stress levels.
Objective: In this pilot study, we aimed to investigate the time trend of stress levels while using TYS for the entire population being studied and whether the individuals’ perceived stress reactivity moderates stress level changes while using TYS.
Methods: Using TYS, stress levels were measured repeatedly with the 4-item version of the Perceived Stress Scale (PSS-4), and perceived stress reactivity was measured once with the Perceived Stress Reactivity Scale (PSRS). A total of 78 nonclinical participants, who provided 1 PSRS assessment and at least 4 repeated PSS-4 measurements, were included in this pilot study. Linear multilevel models were used to analyze the time trend of stress levels and interactions with perceived stress reactivity.
Results: Across the whole sample, stress levels did not change while using TYS (P=.83). Except for one subscale of the PSRS, interindividual differences in perceived stress reactivity did not influence the trajectories of stress levels. However, participants with higher scores on the PSRS subscale reactivity to failure showed a stronger increase of stress levels while using TYS than participants with lower scores (P=.04).
Conclusions: TYS tracks the stress levels in daily life, and most of the results showed that stress levels do not change while using TYS. Controlled trials are necessary to evaluate whether it is specifically TYS or any other influence that worsens the stress levels of participants with higher reactivity to failure.
Individuals decide to use healthcare when the expected benefits outweigh the perceived costs. One of these cost factors in this decision can be stigma. So far, it has not been researched how former soldiers of the German Armed Forces with a service-induced mental illness perceive stigma and how it influences their healthcare use. As stigma is shaped by the socio-cultural context, the setting of the potential healthcare use must be considered. Narrative interviews were conducted with 33 former soldiers with mental health problems. The data were analyzed using a thematic analysis approach, in which codes were formed and emerging themes were systemized. The relationship between stigma and healthcare use was analyzed. Occupational discrimination and social exclusion were experienced in both in the military and civilian context, but stigma functioned differently in each context. In the military context, former soldiers’ self-stigma of mentally ill individuals being weak was in stark contrast to their internalized military standards. This contrast let them avoid disclosure and subsequent healthcare use. In civilian context, the participants perceived 2 different stigma costs: mental illness stigma and former soldier stigma (i.e., stigmatization because of their military past). Both were perceived as barriers to healthcare use. A model, illustrating these different stigma costs in the different contexts, was developed. Further research on the link between stigma and healthcare use of this group is urgently needed.
Following Michael Lipsky's well‐known argument that policy is made in the daily encounters between street‐level bureaucracy and citizens, a growing body of research emphasizes that actors and organizations delivering social and labor‐market policy play a crucial role in welfare‐state politics. Using qualitative data collected at three local employment agencies in Germany, this article explores worker‐client relations as a crucial mechanism through which activation policies are translated into practice. The analysis investigates how caseworkers define their role and their relationships with clients. The findings show that it is essential for caseworkers to achieve client compliance. In such a context, building relationships of trust is a strategic instrument in overcoming possible barriers to co‐operation in the caseworker‐client interaction. Caseworkers develop strategies to create the impression of trustworthiness and to motivate both unemployed clients and employers to become trust‐givers in the caseworker‐client relation. While research has often stressed the dichotomy between disciplining and enabling elements of activation policies, our explorative study shows that persuasion and trust‐building are a further important dimension of the frontline delivery of activation policies. These strategies reflect the importance of emotional aspects of frontline work.
Background Health information systems have developed rapidly and considerably during the last decades, taking advantage of many new technologies. Robots used in operating theaters represent an exceptional example of this trend. Yet, the more these systems are designed to act autonomously and intelligently, the more complex and ethical questions arise about serious implications of how future hybrid clinical team–machine interactions ought to be envisioned, in situations where actions and their decision-making are continuously shared between humans and machines.
Objectives To discuss the many different viewpoints—from surgery, robotics, medical informatics, law, and ethics—that the challenges of novel team–machine interactions raise, together with potential consequences for health information systems, in particular on how to adequately consider what hybrid actions can be specified, and in which sense these do imply a sharing of autonomous decisions between (teams of) humans and machines, with robotic systems in operating theaters as an example.
Results Team–machine interaction and hybrid action of humans and intelligent machines, as is now becoming feasible, will lead to fundamental changes in a wide range of applications, not only in the context of robotic systems in surgical operating theaters. Collaboration of surgical teams in operating theaters as well as the roles, competencies, and responsibilities of humans (health care professionals) and machines (robotic systems) need to be reconsidered. Hospital information systems will in future not only have humans as users, but also provide the ground for actions of intelligent machines.
Conclusions The expected significant changes in the relationship of humans and machines can only be appropriately analyzed and considered by inter- and multidisciplinary collaboration. Fundamentally new approaches are needed to construct the reasonable concepts surrounding hybrid action that will take into account the ascription of responsibility to the radically different types of human versus nonhuman intelligent agents involved.