Search Results (1,272)

The field of communication studies shares significant connections with environmental science, where environmental monitoring constitutes one of the fundamental functions of communication. Based on data from the China General Social Survey (CGSS2021), this study establishes two research models and employs ordered logistic regression to examine the relationships between media usage, environmental water pollution, cognition of environmental policies, and willingness to pay (WTP) for environmental protection. The findings reveal that the perception of water pollution significantly enhances public cognition of environmental policies and WTP. However, the impact of water pollution itself is insignificant, indicating a weak public perception of long-term environmental risks. Both traditional media usage and new media usage significantly improve cognition of environmental policies, with traditional media playing a more pronounced role; yet, media trust does not significantly enhance cognition. Furthermore, new media usage and media trust exhibit a negative impact on WTP for environmental protection, revealing the effects of the “clicktivism” mechanism and the “trust-efficacy perception” negative feedback loop. The negative impact of cognition of environmental policies on WTP further uncovers a “cognition-behavior paradox,” where groups with higher cognition tend to attribute environmental responsibility to the government, thereby reducing their personal WTP. Based on these findings, this paper proposes recommendations including optimizing environmental communication strategies, strengthening public participation, and designing differentiated policies to enhance public environmental awareness and promote the effective implementation of water pollution governance. Full article

A greater understanding of health-promoting factors, such as hope, is crucial for preventing and enhancing the mental health of higher education students. The Herth Hope Index (HHI) is a 12-item tool that has been widely used to assess a comprehensive, non-temporal perception of hope. While this instrument has been used extensively in adult populations, most studies focus on clinical populations. Additionally, the HHI reveals inconsistencies in terms of scale dimensionality and items to be retained. Therefore, this study sought to assess the HHI’s psychometric characteristics in a sample of Portuguese Higher Education students. The person response validity, internal scale validity, unidimensionality, and uniform differential item functioning were assessed using a Rasch rating scale model. A total of 2227 higher education students participated during the e-survey activation period (spring semester of 2020). The mean age of the sample was 22.5 ± 6.2 years (range 18–59 years). Three of the twelve items (#3, #5, and #6) failed to satisfy the established criterion for goodness of fit. Following the elimination of these three items, the resultant nine-item scale exhibited satisfactory item fit to the model, appropriate unidimensionality (52.4% of the variance explained), enough person goodness of fit, sufficient separation, and the absence of differential item functioning. The 9-item version of the HHI had psychometric properties comparable to the original 12-item version. This study also underscores the importance of validated instruments for assessing hope-based interventions in academic contexts. Further research is necessary to explore the potential dimensions inherent to the hope concept and to identify variations in hope profiles among items influenced by cultural attributes. Full article

Background: Formal and informal social support networks are crucial for mental well-being, providing a sense of personal security and safety, especially during times of crisis. Aims: The aim of this study is to examine women’s perceptions of their own experiences alongside their perceptions of their partners’ experiences in the relationship between social support and the sense of danger within intimate relationships, based solely on women’s self-reports. It compares couples experiencing high-intensity conflict (particularly related to separation) with couples from the general population, and explores how the distribution of social support, whether received by the woman, the man, both, or neither is associated with feelings of danger. Methods: The sample comprised 165 women from two subsamples: 70 women from the general population and 95 women engaged in high-intensity intimate conflict, who were undergoing separation proceedings. Data were collected via self-report questionnaires, which included the Multidimensional Scale of Perceived Social Support and a custom questionnaire for Sense of Danger from the Partner. No direct data were collected from male partners; women provided both their own reports and their perceptions of their partner’s experiences. Repeated measures analysis was performed to examine the sense of danger as perceived for both themselves and their partners. Results: The analysis shows that the higher the level of social support, the weaker the sense of danger reported by women for themselves and for their partners. It also reveals that the sense of danger (both self-reported and attributed to the partner) is stronger among couples engaged in high-intensity conflict than among those in the general population, and that women report a stronger sense of danger for themselves than they attribute to their men. Importantly, when social support is provided to only one partner, it is associated with a higher sense of danger in the other partner. Conclusions: Social support has been associated with lower reported feelings of danger in intimate relationships. However, when support is given to only one partner, it may increase the other partner’s sense of danger. These findings highlight the need for balanced support for both partners in order to reduce tension and promote a greater sense of safety during times of conflict. Full article

In an era when AI systems curate increasingly fine-grained aspects of everyday media use, understanding algorithmic fatigue and resistance is essential for safeguarding user agency. Within the horizon of a more algorithmic and hyper-personalized advertising environment, knowing how people resist algorithmic advertising is of immediate importance. This research formulates and examines a structural resistance model for algorithmic advertising, combining psychological and cognitive predictors such as perceived ad fatigue (PAF), digital well-being (DWB), advertising literacy (ADL), and perceived relevance (PR). Based on a cross-sectional survey of 637 participants, the research employs Partial Least Squares Structural Equation Modeling (PLS-SEM) and mediation and multi-group analysis to uncover overall processes and group-specific resistance profiles. Findings show that DWB, ADL, and PR are strong positive predictors of resistance to persuasion, while PAF has no direct effect. PAF has significant indirect influences through both PR and ADL, with full mediation providing support for the cognitive filter function of resistance. DWB demonstrates partial mediation, indicating that it has influence both directly and through enhanced literacy and relevance attribution. Multi-group analysis also indicates that there are notable differences in terms of age, gender, education, social media consumption, ad skipping, and occurrence of digital burnout. Interestingly, younger users and those who have higher digital fatigue are more sensitive to cognitive mediators, whereas gender and education level play a moderating role in the effect of well-being and literacy on resistance pathways. The research provides theory-informed, scalable theory to enhance the knowledge of online resistance. Practical implications are outlined for policymakers, marketers, educators, and developers of digital platforms based on the extent to which psychological resilience and media literacy underpin user agency. In charting resistance contours, this article seeks to maintain the voice of the user in a world growing increasingly algorithmic. Full article

Background and Aim: In 2021, healthcare expenditure in Italy represented 7.3% of the national gross domestic product, with approximately 80% attributed to the management of chronic diseases—an increasing burden associated with population aging. Population stratification tools have emerged as critical instruments for the efficient allocation of healthcare resources, particularly for high-need, high-cost individuals. This systematic review aimed to identify, classify, and evaluate existing population stratification tools based on their characteristics, validation status, and practical applications. Methods: A systematic literature review was conducted in accordance with the PRISMA 2020 guidelines to examine adult population stratification models grounded in healthcare needs. The review encompassed studies retrieved from major scientific databases and included both national and international implementations. Results: The initial search yielded 140,111 records, from which 17 distinct stratification tools were identified. Of these, nine had undergone validation through peer-reviewed studies. Within the Italian context, only six tools were in active use—three of which were developed as region-specific algorithms, while the remaining three employed internationally established software platforms. Conclusions: Population stratification tools provide a robust framework for assessing both clinical complexity and resource utilization, thereby facilitating the design of integrated care pathways and evidence-based policy decisions. In the context of proactive and personalized healthcare delivery, such tools play a pivotal role in enhancing system efficiency, informing strategic planning, and promoting equitable access to care. Full article

Numerical magnitude can bias spatial attention, typically facilitating faster responses to the left for small numbers and to the right for large numbers—an effect traditionally attributed to egocentric spatial mappings. However, in everyday environments, individuals often share space with others, raising the question of whether such spatial–numerical associations can spontaneously reorganize based on another person’s visual perspective. To investigate this, we employed a digit-primed visual detection paradigm in which participants judged the location (left, right, up, or down) of a briefly presented peripheral probe following centrally displayed digits. If numerical magnitude implicitly guides attention, probe detection should be faster when its location is congruent with the digit-induced spatial bias. Critically, in the avatar condition, a task-irrelevant avatar was positioned on the participant’s left side, such that the avatar’s horizontal (left–right) axis corresponded to the participant’s vertical (up–down) axis—an axis along which egocentric numerical biases are typically absent. If participants spontaneously adopted the avatar’s perspective, numerical cues might induce attentional biases along this axis. Results revealed two simultaneous effects: a canonical egocentric SNARC-like effect (small–left, large–right) and a novel allocentric effect (small–up, large–down) emerged along the vertical axis, implicitly aligned with the avatar’s left–right spatial orientation. Numerical extremity enhanced the egocentric SNARC-like effect but had no effect in the allocentric case, pointing to a distinct mechanism rooted in embodied spatial perspective. These findings suggest that numerical magnitude can implicitly map onto both egocentric and allocentric spatial frames, reflecting a implicit and embodied mechanism of social understanding. Full article

Driven by growing public security demands and the advancement of intelligent surveillance systems, person re-identification (ReID) has emerged as a prominent research focus in the field of computer vision. However, this task presents challenges due to its high sensitivity to variations in visual appearance caused by factors such as body pose and camera parameters. Although deep learning-based methods have achieved marked progress in ReID, the high cost of annotation remains a challenge that cannot be overlooked. To address this, we propose an unsupervised attribute learning framework that eliminates the need for costly manual annotations while maintaining high accuracy. The framework learns the mid-level human attributes (such as clothing type and gender) that are robust to substantial visual appearance variations and can hence boost the accuracy of attributes with a small amount of labeled data. To carry out our framework, we present a part-based convolutional neural network (CNN) architecture, which consists of two components for image and body attribute learning on a global level and upper- and lower-body image and attribute learning at a local level. The proposed architecture is trained to learn attribute-semantic and identity-discriminative feature representations simultaneously. For model learning, we first train our part-based network using a supervised approach on a labeled attribute dataset. Then, we apply an unsupervised clustering method to assign pseudo-labels to unlabeled images in a target dataset using our trained network. To improve feature compatibility, we introduce an attribute consistency scheme for unsupervised domain adaptation on this unlabeled target data. During training on the target dataset, we alternately perform three steps: extracting features with the updated model, assigning pseudo-labels to unlabeled images, and fine-tuning the model. Through a unified framework that fuses complementary attribute-label and identity label information, our approach achieves considerable improvements of 10.6% and 3.91% mAP on Market-1501→DukeMTMC-ReID and DukeMTMC-ReID→Market-1501 unsupervised domain adaptation tasks, respectively. Full article

The study examined the relationships between the perceived quality of life (QoL), well-being (WB), and curiosity of older adults. An occasional sample of 199 subjects answered the CASP12 questionnaire (measuring QoL), the 5-DCR scale (measuring curiosity), and a brief demographic questionnaire (referring to gender, age, marital status, and continued interest in the profession). The qualitative layer consisted of twenty in-depth, semi-structured interviews with curious older adults. Findings of the quantitative layer pointed to a substantial association between curiosity and QoL, underscoring the direct and indirect (through curiosity) effects of the demographic factors on QoL, particularly curiosity’s mediating effect on the relationships between continued interest in the profession and QoL. The impact of curiosity, love of learning, and personal attributes on WB were highlighted in the qualitative layer. Implications for cultivating a growth mindset focusing on curiosity across the lifespan and publicizing their role in fostering adaptive aging were discussed. Full article

Mental health professionals are often confronted with clients’ traumatic narratives, which may lead to increased levels of vicarious trauma and burnout, especially when work-related risk factors are present. This study aims to investigate the relationship between vicarious trauma and burnout among mental health professionals in Greece while taking into account work-related and intrapersonal factors using a sample of 266 mental health professionals, who completed the Core Self-Evaluations Scale, Self-Compassion Scale, Vicarious Trauma Scale, and Counselor Burnout Inventory, and provided information about work-related variables, such as caseload, clinical supervision, clinical training, and therapeutic experience. The findings showed that Greek mental health professionals’ burnout positively associated with vicarious trauma and caseload, while negatively associated with intrapersonal factors and work-related factors, namely, years of clinical supervision, clinical training and therapeutic experience. Vicarious trauma negatively correlated with core self-evaluations, self-compassion, and clinical experience. Finally, low core self-evaluations and self-compassion were found to explain greater burnout levels together with higher vicarious trauma and work overload per week, while core self-evaluations were the only variable that moderated the relationship between vicarious trauma and burnout of Greek mental health professionals. These findings indicate that in order to address the interplay between vicarious trauma and burnout, targeted interventions that focus on personal attributes, coping strategies, and systemic organizational support are needed. Full article

Amid the nationwide promotion of fitness and the rapid expansion of China’s sports industry, enhancing spectator satisfaction in sports consumption has become a crucial driver for the industry’s sustainable development. Based on the theory of mind-flow perception, this paper explores the influence of stadium intelligent design on race consumption satisfaction, focusing on the four dimensions of stadium intelligent application perception, personality design perception, digital development perception, and technology integration perception, introduces the mind-flow experience as a mediating variable to construct a theoretical model, and analyzes the questionnaire data of 641 spectators with structural equation modeling. The results show that each perception dimension of intelligent design of stadiums has a significant positive effect on consumer satisfaction. Among them, intelligent applications enhance convenience and interactivity, individual design stimulates emotional resonance and immersion, and digital development and technological convergence optimize the audience’s interactive experience through augmented reality, the Internet of Things, and other technologies. flow experience serves as a key mediator to transform functional attributes into emotional value and immersion experience, significantly enhancing satisfaction. This study contributes theoretical insights and managerial guidance for the integration of AI-driven design, human–technology interaction, and smart construction strategies in modern sports venues. The results have broader implications for enhancing digital user environments and optimizing the infrastructure for next-generation event-based urban development. Full article

Background/Objectives: Men have historically contributed significantly to mental health nursing, particularly in inpatient settings, where their presence has supported patient recovery and safety. Despite this legacy, men remain under-represented in the nursing workforce, and addressing this imbalance is critical to workforce sustainability. This study offers a novel contribution by exploring the lived experiences, motivations, and professional identities of men in mental health nursing, an area that has received limited empirical attention. The aim of the study is to examine the characteristics, qualities, and attributes of mental health nurses who are male, which contributes to their attraction to and retention within the profession. Methods: A qualitative interpretive inquiry was conducted among nurses who were male and either currently or previously employed in mental health settings. Two focus groups were conducted using semi-structured questions to explore their career pathways, motivations, professional identities, and perceived contributions. Thematic analysis was used to identify key themes and patterns in their narratives. Results: Seven participants, with 10–30 years of experience, participated. They had entered the profession through diverse pathways, expressing strong alignment between personal values and professional roles. Five themes emerged and centred on mental health being the heart of health, personal and professional fulfillment, camaraderie and teamwork, a profound respect for individuals and compassion, and overcoming and enjoying the challenge. Conclusions: Mental health nurses who are male bring unique contributions to the profession, embodying compassion, resilience, and ethical advocacy. Their experiences challenge traditional gender norms and redefine masculinity in health care. Fostering inclusive environments, mentorship, and leadership opportunities is essential to support their growth. These insights inform strategies to strengthen recruitment, retention, and the future of mental health nursing. Full article

Fan growth serves as a critical performance indicator for the sustainable development of livestreaming e-commerce (LSE). However, existing research has paid limited attention to this topic. This study investigates the unique interactive advantages of LSE over traditional e-commerce by examining how interactivity drives fan growth through the mediating role of user retention and the moderating role of anchors’ facial attractiveness. To conduct the analysis, real-time data were collected from 1472 livestreaming sessions on Douyin, China’s leading LSE platform, between January and March 2023, using Python-based (3.12.7) web scraping and third-party data sources. This study operationalizes key variables through text sentiment analysis and image recognition techniques. Empirical analyses are performed using ordinary least squares (OLS) regression with robust standard errors, propensity score matching (PSM), and sensitivity analysis to ensure robustness. The results reveal the following: (1) Interactivity has a significant positive effect on fan growth. (2) User retention partially mediates the relationship between interactivity and fan growth. (3) There is a substitution effect between anchors’ facial attractiveness and interactivity in enhancing user retention, highlighting the substitution relationship between anchors’ personal characteristics and livestreaming room attributes. This research advances the understanding of interactivity’s mechanisms in LSE and, notably, is among the first to explore the marketing implications of anchors’ facial attractiveness in this context. The findings offer valuable insights for both academic research and managerial practice in the evolving livestreaming commerce landscape. Full article

In the context of typhlo music therapy, personalized interventions can significantly enhance the therapeutic experience for visually impaired children. Leveraging a data-driven approach, we incorporate action-rule discovery to provide insights into the factors of music that may benefit individual children. The system utilizes a comprehensive dataset developed in collaboration with an experienced music therapist, special educator, and clinical psychologist, encompassing meta-decision attributes, decision attributes, and musical features such as tempo, rhythm, and pitch. By extracting and analyzing these features, our methodology identifies key factors that influence therapeutic outcomes. Some themes discovered through action-rule discovery include the effect of harmonic richness and loudness on expression and communication. The main findings demonstrate the system’s ability to offer personalized, impactful, and actionable insights, leading to improved therapeutic experiences for children undergoing typhlo music therapy. Our conclusions highlight the system’s potential to transform music therapy by providing therapists with precise and effective tools to support their patients’ developmental progress. This work shows the significance of integrating advanced data analysis techniques in therapeutic settings, paving the way for future enhancements in personalized music therapy interventions. Full article

Background: Burnout syndrome (BS) is an occupational phenomenon resulting from chronic workplace stress that has not been successfully managed. Although there are underlying causes associated with personal attributes, it is generally linked to external factors within the work environment. The aim of the present study was to investigate the impact of lifestyle factors on BS and its dimensions, as well as on suicidal ideation among Spanish professionals dedicated to implant dentistry. Methods: A cross-sectional observational study was conducted in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. An electronic survey based on the Maslach Burnout Inventory—Human Services Survey (MBI–HSS) was distributed to members of the Spanish Society of Implants. The data were analysed using descriptive statistical methods. Results: A total of 305 participants (20.9%) responded to the questionnaire. Notably, 10.8% of the professionals reported experiencing suicidal thoughts, a factor significantly associated with the presence of BS. The lifestyle factors associated with BS included the following: not engaging in aerobic exercise for at least 30 min per day (p < 0.05), not having hobbies that facilitate mental disconnection from work (p < 0.001), not following a balanced diet (p < 0.0001), having an insufficient social life (p < 0.0001), and experiencing suicidal ideation (p < 0.01). Conclusions: The surveyed dentists generally reported having healthy lifestyle habits. Nevertheless, one in ten professionals acknowledged having experienced suicidal ideation at some point, highlighting a concerning association with BS. Full article

Knowledge-graph-based recommendation aims to provide personalized recommendation services to users based on their historical interaction information, which is of great significance for shopping transaction rates and other aspects. With the rapid growth of online shopping, the knowledge graph constructed from users’ historical interaction data now incorporates multiattribute information, including timestamps, images, and textual content. The information of multiple modalities is difficult to effectively utilize due to their different representation structures and spaces. The existing methods attempt to utilize the above information through simple embedding representation and aggregation, but ignore targeted representation learning for information with different attributes and learning effective weights for aggregation. In addition, existing methods are not sufficient for effectively modeling temporal information. In this article, we propose MTR, a knowledge graph recommendation framework based on mixture of experts network. To achieve this goal, we use a mixture-of-experts network to learn targeted representations and weights of different product attributes for effective modeling and utilization. In addition, we effectively model the temporal information during the user shopping process. A thorough experimental study on popular benchmarks validates that MTR can achieve competitive results. Full article