University of Cádiz
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Analysis of factors influencing satisfaction with vocational rehabilitation services for young persons with disabilities in Sweden
No full textPurpose: The purpose of this study is to identify what factors influence user satisfaction with vocational rehabilitation services among service users in a Swedish context. Methods: In a randomized control trial, ordinal logistic regression was applied to a dataset of 631 completed questionnaires about the support provided in three different vocational rehabilitation programmes in Sweden—Supported Employment, Case Management and Regular Vocational Rehabilitation. Results: The factors Person-centeredness, Trust in Support Persons, and Experience that the activities help with getting a job were significant factors of satisfaction among service users. The ordinal logistic regression model explained between 34.3% and 49.9% of the variance in the material, depending on the pseudo R2-measure used. Conclusions: Service users who experience vocational support as person-centered, experienced trust in their support persons and that vocational rehabilitation activities help with getting a job are more satisfied with the vocational rehabilitation services than are other service users, independent of the vocational rehabilitation models used. Therefore, a person-centered approach is relevant to include in models’ development and service design of vocational rehabilitation.This research received financial support from the Swedish Social Insurance Agency and the Swedish Public Employment Service, for the data collection. For the preparation of the manuscript, the project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 101026526.</p
A Multi-motive Risk Communication Model for “Making” Crisis Preparedness
No full textThis chapter introduces a mixed-motive model for the practice of risk communication for preparedness in a pre-crisis and pre-disaster phase. Rather than describing how research is done or presenting research results from a single study, this chapter focuses on how communication practitioners can integrate the extensive, but not always congruent, body of existing communication-related risk and preparedness research, in order to understand and reflect on their risk communication practice. The model especially aims to support practitioners in creating a holistic understanding of their own ongoing or future “communication practice” with the goal of increasing society’s level of crisis preparedness and resilience
Politicised or Political : On Agonism and School as ‘Free Time’
No full textAt the centre of this paper is the distinction between a politicised school, and school as a political space. We take note of Papastephanou’s (2005) warning not to make education the passive receiver of political thought. Based on Masschelein and Simons (2013), we criticise the tendency to conceptualise democratic education, particularly agonistic democratic education, as the implementation of political theory in a school context. We draw on their idea of school as free time, to argue that democratic education should envision the classroom as political in itself, that is, as a place for negotiation and renewal, where the ends are not predetermined. When school becomes a place to implement political theory, when it is politicised, then it is stripped of its own political potency. In scholastic terms, it is tamed. In this paper, a fusion of Mouffean agonism with Masschelein and Simons’ conception of school, works to form an understanding of agonistic democratic education as a time and place for the formation of community and for the negotiation of identity, under protection from the non-accountability (Arendt 1961) that characterises scholastic practice
GNN-DM : A Graph Neural Network Framework for Real-World Gas Distribution Mapping
No full textGas distribution mapping (GDM) is essential for industrial safety and environmental monitoring, as it enables real-time hazard detection and air quality assessment. Traditional GDM methods, such as kernel-based techniques, struggle to reconstruct complex gas plume dynamics accurately. While deep learning has shown promise for GDM, two critical challenges hinder its practical use: the scarcity of available training data and the incompatibility of conventional architectures with irregular sensor layouts. To address these limitations, we propose GNN-DM, a graph neural network-based model for GDM that incorporates the relational structure of sensor networks to infer high-resolution maps from minimal, irregular inputs. The model is pretrained on synthetic gas dispersion data generated from measured wind data and fine-tuned on two industrial datasets collected on a ferry car deck and in a hot rolling mill. Compared with established GDM techniques, GNN-DM achieves higher accuracy on synthetic and real-world data, highlighting the potential of graph-based learning for practical gas mapping applications
The travel-hope framework : bridging hope, travel, and well-being
No full textHope is defined as the capability to pursue a desired goal by leveraging one's abilities and potential pathways to goal attainment. This study aims to (1) chart and integrate conceptualizations and operationalizations of hope in travel and well-being research, and (2) investigate the relationship between hope and travel behaviour, as well as its associations with well-being concepts relevant to travel behaviour research, as a base for developing a Travel-Hope Framework. A scoping review was conducted with the following inclusion criteria: (i) adult participants, (ii) validated hope scales, (iii) relevance to travel behaviour and well-being research, (iv) written in English, and (v) peer-reviewed. A systematic search identified 13 studies on hopes conceptualizations and measurement. While none explicitly explored its link to travel behaviour, hope was associated with cognitive, emotional, and social well-being components relevant to travel behaviour research. Building on these insights, we introduce the Travel-Hope Framework, which posits that hope - particularly in the form of travel autonomy and perceived accessibility, and experience and anticipation - is essential for behaviour change and well-being. By illuminating the role of hope in travel decision-making, this framework provides a novel perspective for travel research and policy. Understanding the dynamic interplay between hope, travel and well-being can inform targeted interventions to improve commuting experiences, foster equitable accessibility, and promote sustainable travel choices
Beyond Causal Accuracy : Evaluating Representation Quality in Deep Treatment Effect Estimation
No full textRecent advances in treatment effect estimation (TEE) leverage deep neural networks to learn latent representations that balance treatment and control groups. However, despite their empirical success, we know remarkably little about the learned representations themselves—what structure they capture, why they lead to improved performance, and how far we can trust them. Existing evaluations focus almost exclusively on causal outcome metrics such as PEHE or ATE bias, leaving the representational mechanisms largely unexplored. This lack of understanding limits our ability to explain why models perform well, to identify when they fail, and to design more interpretable or trustworthy causal systems. In this work, we present a comprehensive analysis for representation-level evaluation of TEE methods. We benchmark CFR, DRCFR, TEDVAE and modern disentanglement-driven models using a unified suite of quantitative and graphical measures. Quantitatively, we assess AUC(T | Z) for confounding separability, Mutual Information (MI) retention for informativeness, MMD2 and SW1 for balance, CH-ratio for compactness, and Participation Ratio (PR) for effective dimensionality. Graphically, we analyze bias removal through embedding visualizations, kNN-mixing histograms, covariance heatmaps, and outcome-smoothness histograms—providing an interpretable view of latent geometry. Our analysis shows that modern VAE-based disentangling models yield more structured, balanced, and informative representations, which also correlate strongly with causal reliability. We provide a practical way to see inside TEE representations—revealing hidden biases, interpreting latent behavior, and understanding when conventional embedding properties (such as neighborhood preservation) may actually hinder bias mitigation. Overall, our findings emphasize that evaluating how models learn is essential to understanding why they perform well, paving the way toward more interpretable and trustworthy causal inference
Combining Static and Mobile Sensors on a Quadruped Robot for Adaptive and Responsive Gas Sensing with Low-Cost Sensors
No full textMonitoring of gas emissions is critical for ensuring safety, efficiency, and compliance in industrial plants. Approaches based on stationary sensor networks leave areas of the plant unmonitored and lack adaptability to changing conditions. Combining stationary with mobile sensors on quadruped robots offers a robust approach with improved spatial resolution, adaptability and responsiveness. To streamline the integration of such systems, a unified and open software framework is required. We propose to abstract the low-level implementations, e.g., the robot control layers, using the REST API to facilitate the implementation of high-level tasks such as data visualization and AI-based analysis, thus enabling scalable monitoring solutions. An indoor gas release experiment was conducted, which showed the benefits of the quadruped robot-based and the combined sensing approach.This work was supported by Siemens Foundational Technologies.</p
The Development of a Blueprint for a master program - AI-supported business development in public organizations
No full textThis report presents the development of a blueprint for a Master’s program tailored to public sector professionals without formal technical or programming backgrounds. The blueprint was developed as a part of WASP-ED (The Wallenberg AI and Transformative Technologies Education Development Program) work area 2. The program addresses the growing need for competencies among public servants leading or participating in AI-supported transformation in public organizations. It aims to equip domain experts with foundational AI knowledge and cross-disciplinary insight into the intersection of technology, society, and individuals. The goal is to bridge the knowledge gap between technical experts and sector-specific specialists. The blueprint responds to knowledge gaps identified through a review of academic literature on AI system development and implementation in public organizations, recent reports from public and private entities, and complemented by empirical data collected through workshops, discussions, and interviews with key stakeholders. The suggested program is grounded in both theory and practice, drawing on the Sociotechnical AI systems perspective and the AI System Development Life Cycle (AI-DLC), designed specifically for public sector needs. The initial version of the blueprint was shared with stakeholders including study directors, and others involved in developing Master's-level education in relevant disciplines. It was also evaluated against the WASP-ED curriculum and existing Master’s programs in Sweden. Finally, there is a strong network of informatics researchers across Sweden — including at Örebro University (ÖRU), University of Gothenburg (GU), Linköping University (LiU), and Mid Sweden University — who possess relevant expertise, actively publish in this field, and maintain close collaborations with the public sector. These competencies and networks will play a key role in shaping and further developing the program.WASP-E
Undantagsbestämmelsen – användning och tolkning i praktiken : En kvalitativ studie av ämneslärares och specialpedagogers tolkningar avundantagsbestämmelsen i skollagen
No full textSyftet med denna studie är att bidra med kunskap om hur undantagsbestämmelsen förstås ochtillämpas i skolan av både ämneslärare och specialpedagoger. Fokus ligger på hurämneslärare förhåller sig till styrdokument och skollagens reglering avundantagsbestämmelsen vid bedömning av elever med funktionsnedsättning, samt på hurspecialpedagoger kan stödja lärare i betygsättningsprocessen. Tidigare forskning visar attbedömning av elever i behov av särskilt stöd är ett komplext område präglat av mångadimensioner, där paragrafer i skollag, styrdokument och professionella överväganden oftakrockar. En central utmaning är att tolka vad som ska bedömas och samtidigt avgöra hurelever med funktionsnedsättning kan uppnå betygskriterierna på ett rättvist och adekvat sätt.Emellertid visar tidigare forskning att det saknas tydliga riktlinjer för lärare ibedömningssituationer, vilket riskerar att leda till ojämlik bedömning.Studien utgår från en hermeneutisk ansats, vilket innebär att fokus ligger på att tolka ochförstå hur människor uppfattar och erfar fenomen i sociala sammanhang. Som teoretisktramverk används dilemmaperspektivet, vars syfte är att möjliggöra en fördjupad ochnyanserad förståelse av hur ämneslärare och specialpedagoger tolkar och tillämparundantagsbestämmelsen samt de dilemman som kan uppstå i samband med dessa tolkningar.Uppsatsens metod utgår från en modifierad variant av fokusgruppsintervjuer, vilkagenomfördes med verksamma specialpedagoger och ämneslärare. Resultatet visar attspecialpedagoger i högre grad tenderar att inta ett helhetsperspektiv på elevens skolsituation,medan ämneslärare i större utsträckning separerar stöd och bedömning. Det framkommerockså att specialpedagoger i högre grad ser undantagsbestämmelsen som ett verktyg för attöka elevernas möjligheter till framtida delaktighet i samhället. Vissa ämneslärare, å andrasidan, upplever en osäkerhet inför bestämmelsens praktiska användning.Slutsatsen i denna studie är att det finns ett stort behov av ett ökat samarbete mellanprofessionerna samt tydligare riktlinjer om vad som gäller vid bedömning av elever medfunktionsnedsättning
Older people´s thoughts on meaningfulness in everyday life : A qualitative study
No full textBakgrund: Arbetsterapi syftar till att stödja den enskilde personen i form av att möjliggöra och förbättra aktivitetsutförande. Att ta stöd av den arbetsterapeutiska modellen CMOP-E ger en ökad förståelse om relationen mellan görande, person och miljö. Upplevelsen av meningsfullhet förändras under livets gång och att utföra aktiviteter som ger ett värde kan bidra till meningsfullhet. Äldre är en population som ökar och med åldrandet kan livet förändrats. Vid förlust av meningsfulla aktiviteter på grund av olika anledningar kan äldre riskera att känna en ensamhet. Därmed finns ett behov av att lyfta vad äldre upplever som meningsfullt i vardagen. Syfte: Att beskriva vad som upplevs meningsfullt i vardagen för äldre som bor i eget boende. Metod: Kvalitativ metod med deskriptiv design där data samlades in genom semistrukturerade intervjuer, med tio deltagare som var 70 år eller äldre. Data analyserades med kvalitativ innehållsanalys med induktiv ansats. Resultat: Resultatet resulterade i ett övergripande tema som är: Att ta hand om kropp och själ. Det framkom fyra huvudkategorier med tillhörande underkategorier. Huvudkategorierna är: Olika faktorer som upplevs meningsfulla, personliga egenskaper som bidrar till meningsfullhet, miljöer som skapar en känsla av meningsfullhet och aktiviteter som gynnar känslan av meningsfullhet. Slutsats: Sammanfattningsvis visade resultatet att det var viktigt för äldre att känna sig självständiga och ha en god hälsa. Det var också viktigt att ha ett socialt sammanhang samt befinna sig i trivsamma miljöer. Det upplevdes meningsfullt att utföra aktiviteter som gav ett personligt värde och att ha en acceptans kring olika åldersrelaterade förändringar