Otevřená data na Zenodo

This article explores the evolving role of social workers in the context of increasing digitalisation, focussing on the Czech Republic. Using ecological systems theory and the shift from street-l…

This article explores the evolving role of social workers in the context of increasing digitalisation, focussing on the Czech Republic. Using ecological systems theory and the shift from street-level to screen-level bureaucracy as a framework, we analyse how digital tools are reshaping the relationship between clients and social workers, as well as professional boundaries. Using qualitative data from focus groups and interviews with social workers who support families at risk, we introduce the concept of ‘hotline-level bureaucracy’ to describe a recently emerged practice. In this model, social workers increasingly act as intermediaries between clients and digitalised institutions, often taking on responsibilities due to clients lacking access to technology and digital skills. This shift challenges the empowerment paradigm in social work, burdening practitioners with emotional and cognitive overload, and complicating ethical boundaries. We contend that this transformation necessitates a redefinition of roles, stronger institutional support, and broader structural responses to digital inequality.

The aim of our research was to explore and compare the perspectives of students of social work and workers of homes for older people on the use of social robots in homes for older people. In our resea…

The aim of our research was to explore and compare the perspectives of students of social work and workers of homes for older people on the use of social robots in homes for older people. In our research, we used the standardized UNRAQ questionnaire (Tobis et al. 2021). We distributed a modified questionnaire to (1) students of social work (N = 140) who had taken a gerontology course. We also distributed the questionnaire among (2) workers of homes for older people (N = 133) who had not received any training in gerontotechnology to date. Subsequently, we organised a focus group with (14) students and interviews with (23) workers of homes for older people, including managers, social workers, direct-care workers, and health care workers. The quantitative data collected were evaluated using statistical procedures, and the qualitative data were subjected to thematic analysis. Students who were educated in gerontechnology accepted the use-potential of social robots in social work practice to a higher degree compared to workers who were not educated in this area. The research results showed that education in gerontechnology can play a key role in the adoption of social robots and their subsequent use in practice.

We discuss the possibility of using the PARO robot in the practice of Czech social work. In our research, we used a modified standardized questionnaire and semi-structured interviews. In the research,…

We discuss the possibility of using the PARO robot in the practice of Czech social work. In our research, we used a modified standardized questionnaire and semi-structured interviews. In the research, we compared the evaluation of the PARO robot from the perspective of older adult home staff and older adults. The research helped to identify constructs linked to social robot acceptance that may define benefits and barriers to the implementation of social robots for older adults in social services. If social robots are to be introduced into homes for older adults, we need to respect the perspective of older adults themselves when making decisions about the use of social robots. Furthermore, there is a need for the staff in older adult homes who will be involved in the robot use to be sufficiently educated.

ShinyItemAnalysis (SIA) is an R package and shiny application for an interactive presentation of psychometric methods and analysis of multi-item measurements in psychology, education, and social scien…

ShinyItemAnalysis (SIA) is an R package and shiny application for an interactive presentation of psychometric methods and analysis of multi-item measurements in psychology, education, and social sciences in general. In this article, we present a new feature introduced in the recent version of the package, called "SIA modules," which allows researchers and practitioners to offer new analytical methods for broader use via add-on extensions. SIA modules are designed to integrate with and build upon the SIA interactive application, enabling them to leverage the existing infrastructure for tasks, such as data uploading and processing. They can access and further use a range of outputs from various analyses, including models and datasets. Because SIA modules come in R packages (or extend existing ones), they can be bundled with their datasets, utilize object-oriented systems, or even compiled code. We illustrate the concepts using sample modules from the newly introduced SIAmodules package and other packages. After providing a general overview of building Shiny applications, we describe how to develop the SIA add-on modules with the support of the new SIAtools package. Finally, we discuss the possibilities of future development and emphasize the importance of freely available, interactive psychometric software for disseminating methodological innovations.

This paper focuses on fuzzy--probabilistic IF--THEN rule-based systems, where antecedents encode fuzzy information and consequents represent probability distributions of the output variable. By combin…

This paper focuses on fuzzy--probabilistic IF--THEN rule-based systems, where antecedents encode fuzzy information and consequents represent probability distributions of the output variable. By combining both types of uncertainty within a unified framework, this approach is effective for time series analysis and forecasting.Given a fuzzy covering of the input universe and an output random variable defined on a probability space, the rules state that if the input belongs to a given fuzzy set, then the output is described by a corresponding quantile function. In practice, uniform or generalized fuzzy partitions are typically constructed by shifting equidistant fuzzy sets along the domain axis. The consequent quantile functions are estimated from data as weighted quantiles, where the weights are given by the membership degrees of input values. These weighted quantiles are obtained by minimizing an asymmetric absolute loss functional. The inference mechanism then evaluates the output quantile at a given input as a normalized weighted average of the rule-wise quantile functions.Although fuzzy--probabilistic inference systems have demonstrated effectiveness in various applications, the construction of an appropriate fuzzy partition remains challenging. Uniform partitions are simple but fail to capture complex structures hidden in the data. This motivates the question of whether a data-driven fuzzy partition can better reflect local behaviour under a well-defined criterion. In this paper, we introduce three algorithmic methods for designing non-uniform, data-dependent fuzzy partitions, while a detailed theoretical analysis is left for future work. 

Bipolar affective disorder and depression are among the most prevalent mental health conditions, with recent advances highlighting the role of sensors and computational methods in monitoring them. How…

Bipolar affective disorder and depression are among the most prevalent mental health conditions, with recent advances highlighting the role of sensors and computational methods in monitoring them. However, current Artificial Intelligence (AI)-based systems, while accurate, often lack transparency, limiting their trustworthiness and clinical adoption. Further-more, the state-of-the-art is still missing clear guidelines on how to design advanced human-centric validation approaches for interpretations or explanations of intelligent systems with the aim of paving the way towards trustworthy AI systems ready to be adopted by clinicians. This paper presents a novel evaluation approach integrating supervised learning with fuzzy information granules derived from fuzzy association rules and linguistic summaries to enhance interpretability. Itsmain innovation lies in the human-centric evaluation methodology. Our use case study in the mental health monitoring setting demonstrates the framework’s ability to reveal meaningful relationships between sensor data and mental states. Thus, this work contributes to the development of trustworthy AI systems in compliance with emerging regulatory standards. Our findings confirm that fuzzy logic-based interpretations constructed about the patients’ acoustic features would be beneficial for both clinicians and patients. 75% of respondents agreed that interpretations addressed important aspects of the clinical problem, and 91.7% of respondents agreed that additional interpretations would help psychiatrists in daily patient care. However, evaluations were more critical concerning the clarity and evidential support. Further work should focus on improving the conciseness and clarity of the automatically constructed fuzzy information granules.

Generalized anxiety disorder (GAD) is characterized by persistent, excessive, and difficult-to-control worry. The Contrast Avoidance Model (CAM) proposes that individuals with GAD use worry to sustain…

Generalized anxiety disorder (GAD) is characterized by persistent, excessive, and difficult-to-control worry. The Contrast Avoidance Model (CAM) proposes that individuals with GAD use worry to sustain negative emotional arousal, thereby avoiding sharp negative emotional contrasts that would otherwise follow unexpected adverse events. A virtual reality (VR) protocol was developed to simulate such contrasts by alternating guided relaxation with brief anxiety-inducing scenarios (skyline plank, crowded elevator, and loose dog encounter). This study evaluated the usability and feasibility of this protocol in 20 subclinical adults aged 18–45 who met a screening threshold of GAD-7 ≥ 5, using a Meta Quest 3 headset and Polar H10 heart rate sensor. Exposure segments produced a significant decrease in RMSSD (β = −0.185, p < 0.001), consistent with reduced parasympathetic activity during exposure, whereas heart rate did not differ significantly between conditions. Subjectively, exposure increased SUDS (β = 2.23, p < 0.001) and SAM arousal (β = 1.95, p < 0.001), and decreased SAM valence (β = −2.68, p < 0.001) and dominance (β = −1.70, p = 0.005). Presence scores, cybersickness ratings, and qualitative feedback supported the usability of the protocol and identified concrete design refinements. These results support the feasibility of the protocol and provide a foundation for future controlled clinical evaluation.

This paper presents a general methodological framework for constructing contextaware fuzzy partitions that extend conventional crisp categorizations. The approach isbased on Novák’s theor…

This paper presents a general methodological framework for constructing contextaware fuzzy partitions that extend conventional crisp categorizations. The approach isbased on Novák’s theory of fuzzy contexts and is implemented using the R package lfl. It enables smooth and interpretable transitions between adjacent classes while preserving the original categorical structure. To illustrate the procedure, we apply it to derive fitness-specific fuzzy partitions of Body Mass Index, where the conventional four categories (underweight, normal weight, overweight, obese) are adapted according to individual levels of cardiorespiratory fitness.

The nuggets package provides a flexible and extensible frame-work for discovering interpretable data patterns based on frequent logical conditions. Its designunifies classical association-rule mining …

The nuggets package provides a flexible and extensible frame-work for discovering interpretable data patterns based on frequent logical conditions. Its designunifies classical association-rule mining with linguistic and fuzzy representations, while enablingoptional statistical evaluation for selected pattern types such as conditional contrasts and corre-lations. Pattern generation is driven by support, ensuring efficient mining of relevant conditions,whereas additional quantitative analyses or tests can be seamlessly attached when desired.A major strength of nuggets lies in its extensibility. The framework allows users to definecustom fuzzification schemes and to evaluate an arbitrary R function on every frequent con-dition, thereby enabling the creation of new, user-defined pattern types. This design encour-ages experimentation with alternative logical semantics, statistical measures, and application-specific evaluation criteria, making nuggets not only a tool for applied pattern discovery butalso a research platform for developing new methods.