Search Results (51)

Background/Objectives: Preventive health behaviors are key to disease prevention and health system sustainability; however, population-level factors remain understudied in multilingual regions. South Tyrol, an autonomous multilingual province in Northern Italy, provides a unique setting to examine how sociodemographic and linguistic factors shape preventive behaviors. Methods: A stratified, population-representative survey of 2090 adults (aged ≥18 years) was conducted in South Tyrol in 2024. Preventive behavior was assessed using the validated 16-item Good Health Practices Scale (GHP-16). Key predictors included age, sex, education, living situation, language group, employment in the health/social sector, health literacy (HLS-EU-Q16), patient activation (PAM-10), and the mistrust of professional health information. Weighted statistics and multivariable linear regression were used to identify associations. Results: The GHP-16 scores varied significantly across sociodemographic and psychosocial strata. Female sex, older age, higher education, higher patient activation, and sufficient health literacy were independently associated with greater engagement in preventive behaviors. Patient activation showed the strongest graded effect (β = 1.739). The mistrust of health professionals was inversely associated with behavior (β = –0.050, 95% CI: –0.090 to –0.009). Italian speakers reported higher GHP-16 scores than German speakers (β = 0.377), even after adjusting for covariates. Item-level analysis revealed small but consistent differences, particularly in information-seeking and vaccination behavior. Conclusions: Preventive behaviors in South Tyrol are shaped by demographic, psychosocial, and linguistic factors. Patient activation and health literacy are key modifiable predictors, whereas language group differences suggest structural and trust-related disparities that require tailored public health strategies in multilingual settings. Full article

This study investigates Spanish-language public discourse on YouTube following the unprecedented Iberian Peninsula blackout of 28 April 2025. Leveraging comments extracted via the YouTube Data API and analyzed with the OpenAI GPT-4o-mini model, it systematically examined 76,398 comments from 360 of the most relevant videos posted on the day of the event. The analysis explored emotional responses, sentiment trends, misinformation prevalence, civic engagement, and attributions of blame within the immediate aftermath of the blackout. The results reveal a discourse dominated by negativity and anger, with 43% of comments classified as angry and an overall negative sentiment trend. Misinformation was pervasive, present in 46% of comments, with most falsehoods going unchallenged. The majority of users attributed the blackout to government or political failures rather than technical causes, reflecting a profound distrust in institutions. Notably, while one in five comments included a call to action, only a minority offered constructive solutions, focusing mainly on infrastructure and energy reform. These findings highlight the crucial role of multilingual, real-time crisis communication and the unique information needs of Spanish-speaking populations during emergencies. By illuminating how rumors, emotions, and calls for accountability manifest in digital spaces, this study contributes to the literature on crisis informatics, digital resilience, and inclusive sustainability policy. Full article

Worker safety has been relatively overlooked in the rapidly growing aquaculture industry. To address this gap, industrial accident compensation insurance data—mainly from floating cage and seaweed farming—were analyzed to quantify accident types and frequencies, with a focus on human elements as root causes. Basic causes were selected based on IMO Resolution A/Res.884 and assessed through a worker awareness survey. Fault Tree Analysis (FTA), a Formal Safety Assessment technique, was applied to evaluate risks associated with these causes. The analysis identified organization at the farm site (23.3%), facility and equipment factors (22.8%), and people factors (21.4%) as the primary causes. Among secondary causes, personal negligence (13.2%), aging gear and poor maintenance (11.4%), and insufficient risk training (10.4%) were the most significant. Selective removal of these causes reduced the probability of human element-related accidents from 64.6% to 48.6%. While limited in scope to Korean data and self-reported surveys, the study demonstrates the value of combining quantitative data with worker perspectives. It provides foundational data for developing tailored safety strategies and institutional improvements—such as standardized procedures, multilingual education, and inclusive risk management—for sustainable safety in aquaculture. Full article

Background: While the well-being and working conditions of healthcare professionals are increasingly scrutinized, there remains a critical research gap regarding the quality of life and job satisfaction of plastic surgeons in Switzerland. No prior national study has systematically examined these aspects within this specialty. Objective: This study aims to address this gap by evaluating workload, career satisfaction, and quality of life among Swiss plastic surgeons and trainees, thereby providing evidence to inform systemic improvements in the profession. Methods: A national, multilingual online survey was distributed to all members of the Swiss Society for Plastic Surgery and the Association of Young Plastic Surgeons. A total of 102 plastic surgeons responded (response rate: 22.7%). The survey assessed contractual versus actual working hours, work performed during personal time, mental health indicators (e.g., burnout), and career satisfaction. Descriptive and correlational analyses were conducted. Results: The respondents reported an average of 58 actual versus 49 contractual working hours per week, with an additional 8.1 h spent working during leisure time. Burnout symptoms were present in 29%, and 63% experienced work-related stress during their free time. While 42% wished to reduce their workload, 88.7% would still choose the profession again. Career satisfaction averaged 3.66/5, although 35% rated their salary as inadequate. Notably, 79.8% reported work negatively affecting private relationships, despite 82.65% feeling supported by their environment. Conclusion: This first nationwide assessment highlights the high workload and psychological strain faced by Swiss plastic surgeons. Key priorities include targeted burnout prevention, structural workload reduction, enhanced support for work–life integration (especially among women and younger surgeons), and improved compensation. These measures are essential to sustain the well-being of practitioners and ensure long-term quality in surgical care. Full article

Road safety signboards are now difficult to see due to pollution and harsh weather elements such as snow and fog, which has resulted in more accidents. The problem is especially common in Western countries where snow can block these critical signs. An approach addressing this issue involves a system that uses Radio Frequency Identification (RFID) and Internet of Things (IoT). The real-time alerts that this system sends to drivers improve driver safety in complex environments. For this purpose, an RFID reader is placed in the vehicle, and passive RFID tags are attached to road safety signboards. The reader picks up the signal as a vehicle comes within range, and the warning for the vehicle is sent to the driver. It helps to reduce the number of accidents resulting from poor visibility. In addition, because its multi-lingual audio alerts the drive through speakers and visual warnings displayed on a display screen, the system is accessible to drivers from various regions. To make the system more sustainable, we added some solar panels to the system to cut costs as far as energy efficiency is concerned. The system combines GPS and GSM modules to provide the vehicle position in real time in the cloud. It gives better warnings and helps avoid accidents. In addition to improving road safety, the system offers support for the environment, by limiting emissions and waste of resources caused by accidents. Traffic patterns can thus be studied with the data, creating more efficient and ecofriendly transportation systems. This solution enables a smarter vehicle network that is safer and more sustainable with quick, accurate alerts. Full article

As open science initiatives address the crisis in scholarly communication driven by commercialisation, diamond open access publishing—promoting equity for authors and readers—has emerged as a focal point in open access scholarly publishing. This study examines the landscape of institutional publishing in Croatia, focusing on the community-owned diamond open access model. Through the DIAMAS project survey, which targeted 251 institutional publishers and achieved a response rate of 77, the research identifies the distinct features of Croatian institutional publishing. Institutional publishers are characterised by governance structures, funding challenges, voluntary staffing, and alignment with open science principles. Notable traits include reliance on public funding, use of the national open access journal platform, and a strong diamond open access publishing tradition. Key findings emphasise the critical role of national infrastructure, services, and multilingual publishing. Persistent challenges include meeting indexing criteria, advancing open science practices, and ensuring metadata quality. This study provides a comprehensive mapping of Croatian institutional publishers, offering insights into their strengths and weaknesses while proposing strategies for improvement. The findings underscore the importance of national policy frameworks, capacity building, and international collaboration to ensure the sustainability and visibility of Croatian institutional publishing. Full article

This paper explores a family literacies approach that is being co-constructed in a critical participatory action research project. The project is situated in two culturally and linguistically diverse early childhood settings where the researchers are collaborating with educators and with children and families who have recently arrived in Australia and are learning English as an additional language. In a quest to support and sustain children’s language, literacy, and cultural practices in their home/community languages and in English, we have focused on co-creating multilingual books with the children and their families that are in their languages and about their worlds. This paper examines the affordances and complexities of dialogically co-creating these books for supporting culturally and linguistically sustaining practices in diverse early childhood settings. Full article

This study proposes a comprehensive framework for integrating data-driven approaches into policy analysis and intervention strategies. The methodology is structured around five critical components: data collection, historical analysis, policy impact assessment, predictive modeling, and intervention design. Leveraging data-driven approaches capabilities, the line of work enables advanced multilingual data processing, advanced statistics in population trends, evaluation of policy outcomes, and the development of evidence-based interventions. A key focus is on the theoretical integration of social order mechanisms, including communication modes as institutional structures, token optimization as an efficiency mechanism, and institutional memory adaptation. A mixed methods approach was used that included sophisticated visualization techniques and use cases in the hospitality sector, in global food security, and in educational development. The framework demonstrates its capacity to inform government and industry policies by leveraging statistics, visualization, and AI-driven decision support. We introduce the concept of “institutional intelligence”—the synergistic integration of human expertise, AI capabilities, and institutional theory—to create adaptive yet stable policy-making systems. This research highlights the transformative potential of data-driven approaches combined with large language models in supporting sustainable and inclusive policy-making processes. Full article

This study addresses the need for a comprehensive analysis of the demand for qualified personnel in Spain’s hotel accommodation sector, situating the issue within a context of increasing demand for specialized and highly personalized services in the tourism industry. The objective of this research is to develop a theoretical framework and conduct a preliminary assessment of a model designed to identify and quantify the need for skilled professionals across hotels of different categories and locations. This model considers key factors such as seasonality, required technical competencies, and labour market demands in the tourism sector. The methodology employed combines quantitative and qualitative data analysis from multiple sources, including prior studies, surveys of human resources managers, a review of the academic literature on human resource assessment models in the hotel industry, and a validation survey. Preliminary findings indicate a significant shortage of professionals with advanced expertise in critical areas such as operations management, multilingual customer service, and process digitalization, highlighting the urgent need for a strategic approach to training and talent retention. The conclusions emphasize the importance of adapting workforce planning to the specific demands of Spain’s hotel sector while drawing comparative insights from models in France and the United States. This study proposes strategies to enhance the sector’s competitiveness and sustainability in an increasingly globalized market. Full article

Background: In the wake of Myanmar’s 2021 military coup, the University of Parma, in partnership with Myanmar and Brazilian institutions, developed an asynchronous e-learning program to sustain healthcare education amid severe disruptions. The program aimed to address urgent training needs in emergency medicine, public health management, and mental health, aligning with Sustainable Development Goals. Methods: An educational needs assessment involving 298 surveys and 10 interviews identified training priorities. Based on these findings, a four-module e-learning course was created, covering basic life support, trauma care, pediatric emergencies, and psychological assistance. The course utilized prerecorded high-fidelity telesimulations with multilingual support to ensure accessibility. Evaluation included participant satisfaction using the MSSE questionnaire and knowledge acquisition through post-module quizzes. Results: Over 750 students participated, with significant knowledge acquisition observed—60% scored 8 or higher across all modules. The MSSE questionnaire, completed by 152 students, revealed high satisfaction, with 88% agreeing that the course enhanced clinical reasoning, decision-making, and self-reflection Conclusions: This program demonstrates the value of international partnerships and e-learning in sustaining medical education during crises. High student engagement and strong learning outcomes affirm its efficacy. Future iterations will aim to improve completion rates, refine feedback mechanisms, and expand accessibility. This scalable model offers a blueprint for addressing healthcare training needs in conflict-affected and resource-limited settings, contributing to global health resilience and the achievement of Universal Health Coverage. Full article

This study explores translanguaging as a flexible and adaptive strategy for heritage language transmission within multilingual families residing in Cyprus, Estonia, and Sweden. Using a qualitative approach, the research examines family language policies, parental beliefs, and the linguistic practices of bilingual and multilingual families, where one parent speaks Russian. The findings reveal how translanguaging supports bilingual development by fostering linguistic adaptability, bridging heritage and societal languages, and accommodating diverse sociolinguistic contexts. Parents in each country implement unique strategies, influenced by local linguistic landscapes, educational systems, and resource availability. In Cyprus, some families strictly adhered to structured methods like the One Parent–One Language strategy, while others adopted a more integrative multilingual approach, seamlessly translanguaging between Russian, Greek, and English in their daily interactions. Estonian and Swedish families display pragmatic adaptations, emphasizing translanguaging’s role in promoting the emotional well-being and linguistic identity of family members. However, certain challenges persist, including societal language dominance, literacy and educational resource scarcity, and the potential overuse of translanguaging in formal communication. By comparing these contexts, the study underscores the need for flexible yet deliberate family language policies, institutional support, and community resources to sustain bilingualism in bilingual and multilingual families. This research contributes to understanding translanguaging’s implications for intergenerational language transmission in minority and immigrant settings, offering insights for educators, linguists, and policymakers on fostering linguistic diversity and equity in globalized societies. Full article

Literacy has moved from conventional concepts of reading, writing, and counting skills to proficiency in reading, writing, data, technology, and education for sustainable development. The transition aims to prepare students to engage in a sustainable society and the method aims to cultivate multilingualism and inclusiveness in undergraduate education via a first-year writing (FYW) program. Considering the sustainability of such a transition, this study employs a case study of the FYW program to demonstrate to what extent literacy evolves via pedagogical inclusiveness and multilingualism in higher education. The FYW focuses on how educators can ensure pedagogical inclusiveness by inviting the varied language lingua students have before they arrive at our learning community rather than learning the language itself. The new FYW curriculum includes the World English of our students and embraces multilingualism rather than focusing on error correction so that students fit the norms and rules of American English. The longitudinal data from 2010 to 2020 were collected via five surveys with different approaches, and descriptive statistics were used to analyze them. The outcomes indicated that the new premise generates better course outcomes and fosters students as confident and comfortable writers and readers. The FYW program intersects with other STEM programs to build a more sustainable undergraduate education and cultivate students’ literacy capacity for sustainable development and lifelong learning. Full article

Achieving the Sustainable Development Goals (SDGs) requires collaboration among various stakeholders, particularly governments and non-state actors (NSAs). This collaboration results in but is also based on a continually growing volume of documents that needs to be analyzed and processed in a systematic way by government officials. Artificial Intelligence and Natural Language Processing (NLP) could, thus, offer valuable support for progressing towards SDG targets, including automating the government budget tagging and classifying NSA requests and initiatives, as well as helping uncover the possibilities for matching these two categories of activities. Many non-English speaking countries, including Indonesia, however, face limited NLP resources, such as, for instance, domain-specific pre-trained language models (PTLMs). This circumstance makes it difficult to automate document processing and improve the efficacy of SDG-related government efforts. The presented study introduces IndoGovBERT, a Bidirectional Encoder Representations from Transformers (BERT)-based PTLM built with domain-specific corpora, leveraging the Indonesian government’s public and internal documents. The model is intended to automate various laborious tasks of SDG document processing by the Indonesian government. Different approaches to PTLM development known from the literature are examined in the context of typical government settings. The most effective, in terms of the resultant model performance, but also most efficient, in terms of the computational resources required, methodology is determined and deployed for the development of the IndoGovBERT model. The developed model is then scrutinized in several text classification and similarity assessment experiments, where it is compared with four Indonesian general-purpose language models, a non-transformer approach of the Multilabel Topic Model (MLTM), as well as with a Multilingual BERT model. Results obtained in all experiments highlight the superior capability of the IndoGovBERT model for Indonesian government SDG document processing. The latter suggests that the proposed PTLM development methodology could be adopted to build high-performance specialized PTLMs for governments around the globe which face SDG document processing and other NLP challenges similar to the ones dealt with in the presented study. Full article

Evaluating science learning through written assessments for Multilingual Learners (MLs) can offer a critical source of information for educators aiming to adopt culturally and linguistically sustaining practices. However, without careful planning and design, written assessments for MLs can yield unreliable data. This conceptual article addresses this challenge by first examining the complexities of the written language of science and its impact on student learning. It then explores current literature and presents the Multidimensional Assessment Performance Analysis (MAPA) framework, a multimodal model for analyzing writing answers in multilingual educational contexts. MAPA integrates Systemic Functional Linguistics (SFL) to evaluate language use and Topic Models to allocate students based on their cognitive reasoning and thinking patterns. The article concludes with recommendations for classroom teachers and researchers to enhance assessment practices in science education for MLs. Full article

This paper aims to develop a groundbreaking approach to fostering inclusive smart tourism destinations by integrating generative artificial intelligence (Gen AI) with natural language processing (NLP) and the Internet of Things (IoT) into an intelligent platform that supports tourism decision making and travel planning in smart tourism destinations. The acquisition of this new technology was conducted using Agile methodology through requirements analysis, system architecture analysis and design, implementation, and user evaluation. The results revealed that the synergistic combination of these technologies was organized into three tiers. The system provides information, including place names, images, descriptive text, and an audio option for users to listen to the information, supporting tourists with disabilities. Employing advanced AI algorithms alongside NLP, developed systems capable of generating predictive analytics, personalized recommendations, and conducting real-time, multilingual communication with tourists. This system was implemented and evaluated in Suphan Buri and Ayutthaya, UNESCO World Heritage sites in Thailand, with 416 users participating. The results showed that system satisfaction was influenced by (1) the tourism experience, (2) tourism planning and during-trip factors (attention, interest, and usage), and (3) emotion. The relative Chi-square (χ²/df) of 1.154 indicated that the model was suitable. The Comparative Fit Index (CFI) was 0.990, the Goodness-of-Fit Index (GFI) was 0.965, and the model based on the research hypothesis was consistent with the empirical data. This paper contributions significant advancements in the field of smart tourism by demonstrating the integration of Gen AI, NLP, and the IoT and offering practical solutions and theoretical insights that enhance accessibility, personalization, and environmental sustainability in tourism. Full article