Search Results (2,140)

Climate change disrupts lives globally and poses significant challenges to mental health. Although several scales assess climate anxiety, many either conflate symptoms with coping responses or fail to adequately capture the core symptomatology of anxiety. Hence, this study aimed to develop and validate the Brief Anxiety Scale for Climate Change (BACC), a self-report measure designed to assess symptoms of climate anxiety. A preliminary pool of 21 items was generated based on the diagnostic criteria for generalized anxiety disorder and climate-related stress. Study 1 (n = 300) explored the factor structure via an exploratory factor analysis while Study 2 (n = 400) independently validated the structure via a confirmatory factor analysis (CFA). Analyses of the internal consistency, content validity, and discriminant validity helped refine the scale to a final 13-item version with two factors: cognitive and functional impairment. The CFA results indicated that all the fit indices met the recommended thresholds, and the final version demonstrated excellent internal consistency (Cronbach’s α = 0.92). Additionally, latent correlations revealed that climate anxiety was moderately associated with generalized anxiety and depression. The BACC was developed to identify individuals in the community who experience climate anxiety beyond an adaptive level, thereby promoting sustainable mental health in the context of climate change. These findings suggest that the BACC is a promising tool for assessing climate anxiety. With better identification, mental health professionals, community practitioners, and policymakers can utilize the scale to develop climate-sensitive public health programs and tailored intervention strategies. Full article

Background and Objectives: Investigating the adherence to antihypertensive medication and identifying patients with low adherence allows targeted interventions to improve therapeutic outcomes. Artificial intelligence (AI) offers advanced tools for analyzing medication adherence data. This study aimed to develop and validate several predictive models for non-adherence, using patient-reported data collected via a structured questionnaire. Materials and Methods: A cross-sectional, multi-center study was conducted on 3095 hypertensive patients from community pharmacies. A structured questionnaire was administered, collecting data on sociodemographic factors, medical history, self-monitoring behaviors, and informational exposure, alongside medication adherence measured using the Romanian-translated and validated ARMS (Adherence to Refills and Medications Scale). Five machine learning models were developed to predict non-adherence, defined by ARMS quartile-based thresholds. The models included Logistic Regression, Random Forest, and boosting algorithms (CatBoost, LightGBM, and XGBoost). Models were evaluated based on their ability to stratify patients according to adherence risk. Results: A total of 79.13% of respondents had an ARMS Score ≥ 15, indicating a high prevalence of suboptimal adherence. Better adherence was statistically associated (adjusted for age and sex) with more frequent blood pressure self-monitoring, a reduced salt intake, fewer daily supplements, more frequent reading of medication leaflets, and the receipt of specific information from pharmacists. Among the ML models, CatBoost achieved the highest ROC AUC Scores across the non-adherence classifications, although none exceeded 0.75. Conclusions: Several machine learning models were developed and validated to estimate levels of medication non-adherence. While the performance was moderate, the results demonstrate the potential of AI in identifying and stratifying patients by adherence profiles. Notably, to our knowledge, this study represents the first application of permutation and SHapley Additive exPlanations feature importance in combination with probability-based adherence stratification, offering a novel framework for predictive adherence modelling. Full article

Aside from a few passages addressing animals or the environment, Confucian philosophy appears to lack an environmental ethics perspective. Li Zhehou’s (李澤厚) contemporary work in Confucian philosophy continues this lacuna by limiting his understanding of community to the human realm. Using the common liberal humanism that limits moral actions to the interpersonal human realm misses the importance of inclusive moralities such as animal rights and environmental ethics. I propose that if we return to the original shared common cultural roots of Confucian and Daoist philosophy that a Confucian understanding of the natural world can embrace the non-human environment within the scope of Confucian morality. Extricating ideas from the Yijing, the Shijing, Xunzi, Dong Zhongshu, Wang Chong, and later scholars, the concept of the mutual resonance and response (ganying 感應) between the natural world and humans developed into the unity of heaven and humanity (tianren heyi 天人合一). An inclusive Confucian depth ecology opens new ways of thinking that can be deployed to envision deeper dimensions for understanding the self’s inner life, its connections to the outer life of the self–other relationship, and its extension to a kin relationship with the environment. This paper explores how these old and new ways of thinking can change our behavior and change our moral interactions with others including the environment and thereby enhancing freedom as an achievement concept derived from graceful moral action. Full article

All individuals hold beliefs about their ability to successfully perform specific tasks. These beliefs, known as self-efficacy, play a key role in guiding and motivating human behavior. In education, both teachers’ self-efficacy beliefs and the collective efficacy shared by teachers within a school have been linked to improved performance, well-being, and job satisfaction among students and educators. While these constructs have been widely studied in various countries and contexts, little is known about them in Mexico, the country with the largest Spanish-speaking population worldwide. This study is the first to examine the relationship between teacher self-efficacy (TSE), collective efficacy, and other teacher- and school-level variables in Mexico. Given the absence of psychometrically robust instruments to assess collective efficacy among Spanish-speaking teachers, the Collective Teacher Beliefs Scale (CTBS) was first adapted into Spanish, and its psychometric properties were evaluated. Subsequently, multilevel analyses incorporating teacher- and school-level factors revealed that professional development on multicultural communication, classroom autonomy, and collaboration, at the teacher level, and collective efficacy and stakeholder participation, at the school level, were significant predictors of TSE. Finally, implications for future practice and policy are discussed. Full article

I argue that the universally accepted assumption that in realist fiction a character narrator’s narration contributes to their characterization needs to be complicated. Working with a conception of narrative as rhetoric that highlights readerly interest in the author’s handling of the mimetic, thematic, and synthetic components of narrative, I suggest that the question about narration as characterization is one about the relation between the mimetic (character as possible person) and synthetic (character as invented construct) components. In addition, understanding the mimetic-synthetic relation requires attention to issues at the macro and micro levels of such narratives. At the macro level, I note the importance of (1) the tacit knowledge, shared by both authors and audiences, of the fictionality of character narration, which means authors write and readers read with an interest in its payoffs; and of (2) the recognition that character narration functions simultaneously along two tracks of communication: that between the character narrator and their narratee, and that between the author and their audience. These macro level matters then provide a frame within which authors and readers understand what happens at the micro level. At that level, I identify seven features of a character’s telling that have the potential to be used for characterization—voice, occasion, un/reliability, authority, self-consciousness, narrative control, and aesthetics. I also note that these features have their counterparts in the author’s telling. Finally, I propose that characterization via narration results from the interaction between the salient features of the character’s telling and their counterparts in the author’s telling. I develop these points through the analysis of four diverse case studies: Mark Twain’s Huckleberry Finn, Robert Browning’s “My Last Duchess,” Nadine Gordimer’s “Homage,” and Ernest Hemingway’s A Farewell to Arms. Full article

This study presents a methodology developed in collaboration with the Colombian National Mining Agency, aimed at enhancing the economic and productive activity of small-scale miners in Colombia through the promotion of associativity. Despite persistent challenges in the formalization and sustainable development of the artisanal mining sector, fostering associative models offers a pathway towards a more sustainable mining industry, aligned with current national policies. The proposed roadmap, designed to achieve this objective, is divided into three sequential phases. The first, the Baseline Survey, focuses on comprehensively understanding the initial socio-economic and operational conditions of mining communities. This is followed by Participatory Strategic Planning, which involves projecting the long-term role and development of mining associative figures. Finally, the Annual Operational Planning and Execution phase concentrates on the concrete implementation of activities designed to achieve sustainable organizational goals. During the design and initial implementation of this roadmap, we found that continuous support and tailored training programs are essential for mining communities. These programs are critical for fostering the development of collective skills and strengthening community ties within mining organizations. The findings highlight that by strengthening collective capabilities and community ties, mining organizations can enhance their self-management capacities and significantly contribute to the economic development of their regions. This approach addresses key challenges in the sector by promoting a more organized and resilient operational framework. The implementation of a participatory methodology, coupled with specific organizational strengthening programs, coordinated execution, and continuous monitoring, provides a viable route towards a more sustainable and formalized small-scale mining sector in Colombia. This roadmap offers a practical framework for fostering self-managed and economically contributing mining organizations. Full article

Reinforcement learning (RL), a branch of artificial intelligence (AI), is becoming more popular in a variety of application fields such as games, workplaces, and behavioral analysis, due to its ability to model complex decision-making through interaction and feedback. Traditional systems for personality and behavior assessment often rely on self-reported questionnaires, which are prone to bias and manipulation. RL offers a compelling alternative by generating diverse, objective behavioral data through agent–environment interactions. In this paper, we propose a Reinforcement Learning-based framework in a game environment, where agents simulate personality-driven behavior using context-aware policies and exhibit a wide range of realistic actions. Our method, which is based on the OCEAN Five personality model—openness, conscientiousness, extroversion, agreeableness, and neuroticism—relates psychological profiles to in-game decision-making patterns. The agents are allowed to operate in numerous environments, observe behaviors that were modeled using another simulation system (HiDAC) and develop the skills needed to navigate and complete tasks. As a result, we are able to identify the personality types and team configurations that have the greatest effects on task performance and collaboration effectiveness. Using interaction data derived from self-play, we investigate the relationships between behaviors motivated by the personalities of the agents, communication styles, and team outcomes. The results demonstrate that in addition to having an effect on performance, personality-aware agents provide a solid methodology for producing realistic behavioral data, developing adaptive NPCs, and evaluating team-based scenarios in challenging settings. Full article

Background/Objectives: With aging linked to increased oral health conditions, the communication skills of dental professionals are vital to ensure patient satisfaction and improve the quality of geriatric dental care. This cross-sectional study evaluated geriatric patients’ satisfaction with communication at a university dental clinic, comparing interactions with dentists and fifth-year dental students. Methods: A self-report questionnaire was administered to 111 patients, assessing sociodemographic data, general health, and satisfaction across six communication dimensions: verbal and nonverbal communication, empathy, respect, problem-solving, and support materials. The data were analyzed using Mann–Whitney U tests (α = 0.05). Results: The geriatric patients exhibited higher levels of satisfaction when interacting with dentists compared to students, particularly in the domains of nonverbal communication (p = 0.007), empathy (p = 0.035), and respect (p = 0.017). However, no statistically significant differences (p > 0.05) were observed in terms of verbal communication, problem-solving, and support materials. Conclusions: The geriatric patients demonstrated greater satisfaction with interactions with practicing dentists. These findings indicate that an attending dentist’s clinical experience enhances interpersonal interactions with geriatric patients. Therefore, developing the interpersonal skills of future dentistry professionals, as well as adapting communication to the needs of the elderly, is essential to provide more satisfactory experiences in geriatric dental care. Full article

In the context of the new developmental philosophy, this study aimed to address the bottleneck of regional sustainable development; it constructs a three-system evaluation indicator system for Industrial Structure Optimization (ISO), Ecological Environment Management (EEM), and Socio-economic Development (SED), based on panel data from 20 cities in the Western Taiwan Straits Economic Zone between 2011 and 2023. To reveal how the synergistic development of the three subsystems in different domains can achieve sustainable development through their interactions and to analyze the dynamic patterns of the three subsystems, this study employed the panel vector autoregression (PVAR) model to examine the interactions between subsystems. Additionally, drawing on the framework of evolutionary economics, the study quantified the temporal evolution and spatial characteristics of the coupling coordination level among the three subsystems based on the results of the degree of coupling coordination model. The results indicate the following: (1) ISO shows a significant upward trend, EEM slightly declines, and SED experiences minor fluctuations before accelerating. (2) ISO, EEM, and SED exhibited self-reinforcing effects. (3) The degree of coupling, coordination, and coupling coordination all exhibit a trend of “fluctuating and increasing initially, followed by steady growth”. The spatial patterns of the degree of coupling, coordination, and coupling coordination have shifted from “decentralized” to “centralized”, with clear signs of synergistic development. (4) The difference in the degree of coupling coordination along the north–south direction remained the primary factor contributing to inter-regional disparities. Regions with the higher degrees of coupling coordination were concentrated in the southeastern coastal areas, while those with the lower degrees of coupling coordination appeared in the northeastern mountainous areas and southwestern coastal areas. (5) The spatial connection in the strength of the degree of coupling coordination has gradually increased, with notable intra-provincial connections and weakened inter-city connections across the province. The study’s results provided decision-making references for the construction of a sustainable development community. Full article

Based on ecological systems theory, adolescents’ school climates can influence family interactions. In this study, it was tested whether associations between adolescents’ and parents’ perceptions of school climate in 7th grade (Wave 1) and adolescents’ later psychological functioning in 11th grade (Wave 4) were partially mediated by parental self-efficacy and parenting practices when the adolescents were in 8th grade (Wave 3). Path analyses were conducted in MPlus v. 7.4. Among 660 Black American families from the Maryland Adolescent Development in Context Study, adolescents’ positive perceptions of school climate were directly related to fewer internalizing and externalizing symptoms, higher resourcefulness, and higher self-esteem. Adolescents’ perceptions of school climate were also indirectly related to their externalizing symptoms through parent–adolescent communication and conflict. Adolescents who reported more positive school climates reported higher parent–adolescent communication and lower parent–adolescent conflict. Adolescents’ reports of school climate were also indirectly associated with self-esteem. Parents’ perceptions of school climate were not directly related to adolescents’ psychological functioning but were directly related to parental self-efficacy and parent–adolescent communication. They were also indirectly related to adolescents’ externalizing symptoms through parental self-efficacy. Parental self-efficacy was positively related to parent–adolescent communication and parents’ home-based school involvement. Overall, the findings highlight the role of school context in adolescents’ psychological functioning and family processes. Full article

Rural tourism has emerged as an efficient strategy for rural revitalization while having various impacts on rural governance. Previous studies predominantly focused on the social implications of rural tourism and its impact on institutional arrangements while neglecting the influence of rural tourism on collective action in rural governance. This study employed a social–ecological system (SES) framework to investigate the influence of rural tourism on rural collective action, utilizing survey data from 22 provinces (autonomous regions and municipalities directly under the central government), 178 villages, and 3282 rural households across China. The findings revealed that rural tourism exerted a positive influence on collective action, primarily through labor force reflow mechanisms. Specifically, the leadership of village cadres had a moderating role in enhancing this positive correlation. Further analysis revealed significant heterogeneity in tourism governance effects: non-plain regions and villages with medium to low economic development levels exhibited substantial improvements in collective action, whereas plain areas and economically advanced villages may manifest potentially negative impacts. Theoretically, this study contributes to elucidating tourism-driven self-governance mechanisms by applying the SES framework, thereby transcending the traditional dualistic debate between state-market and development-governance paradigms. Practically, we propose institutional designs that embed collective action mechanisms into the coupled synergistic development of rural tourism and community governance, thereby activating endogenous motivations for rural self-governance. Full article

Background: Primary progressive aphasia (PPA) is a neurodegenerative disease characterised by progressive impairment of speech and language abilities. Intensive speech and language teletherapy combined with remotely supervised, self-administered transcranial direct current stimulation (tDCS) may be suited to remove barriers to accessing potentially effective treatments, but there is only limited evidence on the feasibility of this combined approach. Methods: This pilot case series investigated the feasibility, tolerability and preliminary efficacy of a novel telerehabilitation programme combined with home-based, self-administered tDCS for people with primary progressive aphasia (pwPPA). The intervention programme was co-developed with pwPPA and their caregivers, to reflect their priorities regarding treatment content and outcomes (i.e., naming, functional communication). Results: Two pwPPA successfully completed the telerehabilitation intervention with daily naming training and communicative-pragmatic therapy paired with tDCS, over 10 consecutive workdays. Caregivers assisted in the setup of equipment required for teletherapy and home-based tDCS. Participants successfully completed the programme with a 95% completion rate. Home-based tDCS was well tolerated. Both participants showed improvements in naming and communication, suggesting preliminary efficacy of the intervention. Conclusions: Overall, this study demonstrates the feasibility and potential benefit of a novel, easily accessible and patient-relevant telerehabilitation intervention for pwPPA, which requires confirmation in a future larger-scale exploratory trial. Full article

To alleviate the impact of economic and environmental detriments caused by the increased demands of electric vehicle battery production and disposal, the use of spent batteries in second-life stationary applications such as energy storage for renewable sources or backup power systems, offers many benefits. This paper focuses on reducing the energy consumption cost and greenhouse gas emissions of Internet-of-Things-enabled campus microgrids by installing solar photovoltaic panels on rooftops alongside energy storage systems that leverage second-life batteries, a gas-fired campus power plant, and a wind turbine while considering the potential loads of a prosumer microgrid. A linear optimization problem is derived from the system by scheduling energy exchanges with the Ontario grid through net metering and solved by using Python 3.11. The aim of this work is to support Sustainable Development Goals, namely 7 (Affordable and Clean Energy), 11 (Sustainable Cities and Communities), 12 (Responsible Consumption and Production), and 13 (Climate Action). A comparison between a base case scenario and the results achieved with the proposed scenarios shows a significant reduction in electricity cost and greenhouse gas emissions and an increase in self-consumption rate and renewable fraction. This research work provides valuable insights and guidelines to policymakers. Full article

Bilingual communication is increasingly prevalent in this globally connected world, where cultural exchanges and international interactions are unavoidable. Existing automatic speech recognition (ASR) systems are often limited to single languages. However, the growing demand for bilingual ASR in human–computer interactions, particularly in medical services, has become indispensable. This article addresses this need by creating an application programming interface (API)-based platform using VOSK, a popular open-source single-language ASR toolkit, to efficiently deploy a self-evaluated bilingual ASR system that seamlessly handles both primary and secondary languages in tasks like Mandarin–English mixed-speech recognition. The mixed error rate (MER) is used as a performance metric, and a workflow is outlined for its calculation using the edit distance algorithm. Results show a remarkable reduction in the Mandarin–English MER, dropping from ∼65% to under 13%, after implementing the self-evaluation framework and mixed-language algorithms. These findings highlight the importance of a well-designed system to manage the complexities of mixed-language speech recognition, offering a promising method for building a bilingual ASR system using existing monolingual models. The framework might be further extended to a trilingual or multilingual ASR system by preparing mixed-language datasets and computer development without involving complex training. Full article

Social media applications (SMAs) stand at the forefront of emerging technologies in language education due to their transformative potential to enhance English speaking skills. Unlike traditional tools, SMAs leverage features like real-time communication, multimedia integration, and global connectivity, fostering authentic, meaningful interactions in socially engaging environments. Although learning management systems (LMSs) such as Brightspace have been predominantly used to conduct English language learning activities, including peer feedback, SMAs like Facebook offer unique affordances that may further promote student engagement and confidence. Developing public speaking skills is critical for effective communication in English language education; however, many students struggle with self-efficacy, speaking anxiety, and providing meaningful feedback to peers. Given the potential benefits of SMAs, this study explored their comparative impact on three dimensions of English language learning: English public speaking self-efficacy, peer feedback self-efficacy, and English speaking performance. This quasi-experimental study used a pretest–posttest design with undergraduate students (n = 15) at a large Midwestern university in the United States. Independent sample t-tests were conducted to compare outcomes between the Facebook and Brightspace groups. The results revealed that both SMAs and LMSs were equally effective platforms for peer feedback activities, with no significant differences found between groups across the three targeted areas of learning. Full article