St. Pölten University of Applied Sciences

International collaboration in allied health and nursing education faces a long-standing paradox: although healthcare practice is increasingly global, professional education remains largely national or local. Regulatory constraints, the historical framing of health professions as task-based rather than academic, gendered perceptions of nursing as supportive rather than leadership-oriented, and structural inequities all limit cross-border collaboration and physical student mobility. In response, the CRIISIS (Connecting and Reflecting in Student International Interactive Study Groups) Collaborative Online International Learning (COIL) model was developed to reimagine internationalization through equity, relational learning, and reciprocity. Grounded in Kolb’s Experiential Learning Theory, the model integrates students’ personal and family health crisis narratives into a structured cycle of reflection, comparative analysis, and action within international teams. Students engage in virtual proximity, intentional relationship-centered interaction across distance, using dialogue, shared inquiry, and collaborative problem-solving to connect individual experiences to global health challenges and the United Nations Sustainable Development Goals. Evaluation of three implementation cycles demonstrates that the CRIISIS COIL model fosters cultural humility, communication adaptability, leadership within complexity, and narrative competence. Students develop deeper awareness of how social and structural factors shape health and family experiences across societies. By valuing lived experience as legitimate knowledge and promoting horizontal partnership across institutions, the model challenges traditional hierarchies in global education. Ultimately, CRIISIS positions global learning not as an extractive exchange, but as an ethical, relational, and equity-oriented process that prepares health professionals to navigate and address health inequities in an interconnected world.

Interpreting data visualizations is an essential skill in today's education, yet students often struggle with understanding unfamiliar formats. This study investigates how four learning materials - textbook, comic, video, and game - affect middle- and high school students' ability to interpret line charts, area charts, stacked area charts, and stream graphs. We conducted a comparative classroom study with 68 students, using pre- and post-tests, worksheet activities, and group discussions to assess learning outcomes and understanding. Our results show statistically significant improvement in students' understanding of stacked area charts and stream graphs, while no significant differences between the learning materials were found. This suggests that more factors than initially anticipated - such as engagement, motivation and active learning strategies - influence the learning outcome. The analysis of the worksheets revealed that while students could infer surface-level insights from charts, over 70% struggled to identify underlying patterns or relationships. Additionally, a common challenge across all learning materials was reading fatigue, which often led students to skim content, disengage, or misinterpret key information. These findings highlight the need for educational tools and approaches that foster deeper understanding of unfamiliar visualizations, reduce cognitive load, and encourage active engagement.

Introduction:A predominantly plant-based dietary concept was developed in the course of the NUMOQUA study (Nutrition and Movement to Improve Quality of Life in Patients with Knee Osteoarthritis) as part of the interdisciplinary therapeutic intervention for patients with knee osteoarthritis (OA). This study included focus group discussions (FGDs), which aimed to identify perceived barriers and facilitators for following these dietary recommendations. Methods:In total, 4 FGDs were held with a total of 24 participants aged 50-75 years with mild to moderate knee OA, who belonged to the intervention group of the NUMOQUA study and followed the plant-based diet. FGDs were recorded, transcribed, and coded using an adapted version of the socio-ecological framework in MAXQDA (Max Qualitative Data Analysis) software. Thematic areas were identified by collating data relevant to each code, code group, and the four spheres of influence (individual, interpersonal and social, environmental, and policy). Results:Barriers for adopting the dietary recommendations primarily involved the limited availability of suitable options when eating out, adherence to traditional eating habits, and prioritizing family members' preferences. Facilitators included family support, goal-setting, establishing a routine, individual approaches, and motivation derived from improved physical health. Discussion:The results reflect the diversity of influencing factors at the individual, interpersonal and social, and environmental levels, and provide important information for future dietary interventions for those affected by knee OA and other non-communicable diseases, particularly as cardiovascular diseases and type 2 diabetes. Clinical trial registration:https://clinicaltrials.gov/study/NCT05955300, identifier NCT05955300.

Recently, in the social choice literature, much attention has been given to the question of avoiding underrepresentation in approval-based multi-winner voting. In this paper, we explore the largely overlooked complementary question of avoiding overrepresentation. This has not been explored systematically, despite being a desirable property with concrete applications. Intuitively, overrepresentation happens when a group determines a disproportionately large part of the committee, thereby exceeding the group's quota. We formulate a strong and appealing axiom for avoiding overrepresentation, called justifiable upper quota (JUQ). We introduce a generalization of Thiele rules, composite Thiele rules, and characterize the unique rule in this class satisfying our axiom. This rule, Adams-AV, which naturally extends Adams' apportionment method, has not been studied before. Additionally, we introduce a polynomial-time rule that satisfies JUQ. Furthermore, we introduce justified near quota, an axiom that balances avoiding under- and overrepresentation. It characterizes the unique Thiele rule extending the Sainte-Laguë apportionment method. Finally, we analyze the compatibility of our axioms with established proportionality notions such as EJR+.

Epilepsy is a pathological condition characterized by excessive brain electrical activity, resulting in sudden seizures perceived by patients, which can lead to sudden loss of consciousness and convulsions. Diagnosing epilepsy requires the physician to manually inspect the electroencephalography of the patient, a typically time-consuming task for medical staff. In this paper, we propose a method for the potential identification of epileptic seizures, by generating from an electroencephalography two different types of wavelet transforms i.e., the Morlet and the Mexican Hat, obtained by previously transforming the waveform into an audio file. We exploit a hybrid quantum-classical model for the binary classification of an electroencephalography as belonging to an epileptic patient or a not epileptic one, directly comparing the proposed model with state-of-the-art convolutional neural networks, in order to demonstrate the effectiveness of the proposed method, obtaining that the proposed hybrid model is able to obtain better performances from the accuracy point of view with respect to convolutional neural networks.