Search Results (462)

In Mexico, the high prevalence and mortality rates associated with breast cancer (BC) constitute a critical public health challenge that demands context-specific preventive measures. This study proposes an integrative framework for predicting BC risk based on a biopsychosocial model. We hypothesize that emotional suppression and repression act as key neuroendocrine disruptors and predisposing factors within the Mexican female population. To test this, we systematically compared the predictive performance of various machine learning classification models using the clinical, psychological, and combined profiles of 110 women. These models were evaluated with and without the application of a robust evolutionary algorithm: Differential Evolutionary Linear Discriminant Analysis for Feature Extraction $(DE-LD{A}_{FE}$). The results demonstrated that integrating clinical and psychological data into a combined latent space significantly improved the performance of the classification algorithms. The Artificial Neural Network achieved the highest metrics (0.9975 Precision; 0.9976 F1-score). However, due to the inherent “black-box” nature of these models (limited clinical interpretability), the Decision Tree emerged as the optimal practical alternative, providing highly competitive (0.8874 Precision; 0.8853 F1-score) and interpretable results. These findings provide empirical evidence that psychological factors, rather than being mere incidental comorbidities, could be associated with the etiology of breast cancer and be used as risk factors in predicting the disease. Ultimately, this AI-driven biopsychosocial screening model offers a scalable, low-cost, and context-adapted risk assessment tool for early BC diagnosis in Mexican women. Full article

Background/Objectives: The intersection of mental health and acute coronary syndromes has become an increasingly prominent area of cardiovascular and psychosomatic research, yet its temporal dynamics and intellectual structure remain incompletely characterized. Methods: This study analyzed 13,646 peer-reviewed documents spanning five decades, employing advanced changepoint detection (PELT) algorithms, network visualization (VOSviewer), and bibliometric performance metrics (Bibliometrix) to quantify the evolution of the mental health–ACS intersection. Results: Statistical analysis identified two robust inflection points at 1990 and 2005 that demarcate distinct developmental periods. The 1990 breakpoint marked an important transition, although additional metadata-completeness analysis indicated that part of the increase from 72 to 142 publications may reflect improved availability of non-title Topic-field metadata in WoSCC around 1990–1991. The 2005 breakpoint represented the most critical transition (Cohen’s d = 4.05, p < 0.000001), initiating exponential growth from 349 to over 600 annual publications by 2022 and coinciding with growing research attention to psychiatric comorbidity within ACS literature. Keyword co-occurrence networks revealed a shift in research focus: early publications predominantly addressed mental health as a psychological reaction to cardiac events, whereas more recent publications increasingly frame depression, anxiety, and PTSD alongside mechanistic constructs such as inflammatory pathways, autonomic dysfunction, and platelet reactivity. Although seminal intervention trials (i.e., ENRICHD, SADHART) established pharmacological safety and symptom improvement, keyword analyses indicate that following these trials, research attention increasingly shifted toward precision psychiatry concepts and mechanistic pathway elucidation. Conclusions: These findings provide a quantitative map of how publication activity at the mental health–ACS intersection has evolved, offering a structured basis for identifying under-researched areas and informing future research agendas. Full article

Health and spa tourism is a rapidly growing sector that merges traditional healing with modern innovations to meet increasingly diverse client needs. Understanding professionals’ perspectives is crucial for developing sustainable strategies that enhance service quality, organizational performance, and long-term business viability. Drawing on qualitative narrative analysis and thematic network analysis, this study explores the key factors that spa tourism professionals in Băile Felix—the largest spa resort in Romania—associate with business success, competitive differentiation, and sustainable development. Data were collected through semi-structured interviews with 41 entrepreneurs and managers who provided detailed narratives on their strategic goals and market positioning. Rather than measuring customer psychological constructs directly, this study captures professionals’ expert attributions regarding service quality, staff professionalism, infrastructure investment, and economic objectives, and interprets these as managerial perceptions grounded in operational experience. Five research propositions guided the interpretive analysis: (P1) professionals narratively associate service quality and treatment diversity with perceived business performance and guest retention signals; (P2) staff professionalism and attitude are attributed as the primary drivers of competitive differentiation; (P3) infrastructure investment and innovation are framed as prerequisites for sustaining market positioning; (P4) the identified themes form a structurally interconnected network with key bridging nodes; and (P5) professional narratives reveal tensions between short-term economic objectives and longer-term commitments to service quality and sustainability. Thematic network analysis identified four central constructs—service quality and treatment diversity, staff professionalism and attitude, innovation and infrastructure investment, and economic and development objectives—and mapped 16 interconnected sub-themes, with modularity analysis (Q = 0.42) confirming a moderately cohesive structure. Sustainable innovation was operationalized across environmental efficiency, social value, and economic resilience dimensions, and found to be embedded systemically across multiple thematic clusters rather than treated as an isolated practice. The originality of this study lies in integrating narrative and thematic network analysis to reveal how these constructs co-evolve within a sustainability-oriented system, offering a novel methodological lens for spa tourism research in post-transitional Central and Eastern European contexts. Findings provide actionable insights for spa managers, policymakers, and investors seeking to balance modernization with tradition in resource-constrained destinations. Full article

Self-harm represents a significant mental health concern during adolescence and is associated with various psychological risk factors. The present exploratory probe examines the mediating role of perceived social support in the relationship between self-esteem and self-harm among adolescents. The sample consisted of 155 adolescents aged 13 to 18 years (M = 16.35, SD = 1.73). Self-esteem was measured using the Rosenberg Self-Esteem Scale (RSES), self-harm was assessed using a modified version of the Self-Harm Inventory (SHI), and perceived social support was measured using the Multidimensional Scale of Perceived Social Support (MSPSS). Data were analyzed using correlation analysis, linear regression, and mediation. More than half of the participants (53.5%) reported repeated engagement in self-harming behavior. Self-esteem was significantly negatively associated with self-harm (ρ = −0.508, p < 0.001) and explained approximately 22% of the variance in self-harm. Mediation analysis indicated that perceived social support partially mediated the relationship between self-esteem and self-harm. Lower self-esteem was associated with lower perceived social support, which in turn predicted higher levels of self-harm. The indirect effect was significant (B = −0.31, 95% BootCI (−0.63, −0.09)). These findings highlight the protective role of perceived social support and suggest that strengthening adolescents’ self-esteem and social support networks may contribute to the prevention of self-harm. Full article

Urban heritage structures are most commonly understood as memorial artifacts, tourism assets, or redevelopment resources. While this common view acknowledges cultural and economic value, it overlooks a deeper function of heritage within the long evolution of human settlements. This paper advances a counter thesis: in addition to its historic contingencies and power relationships—which are real, but only part of the picture—urban heritage embodies valuable but often hidden intelligence that is highly relevant to contemporary urban challenges. Specifically, heritage environments encode useful structured information about spatial configurations that have gained adaptive value over time in a process known as stigmergy. Drawing on complexity science, network theory, the mathematics of symmetry, and theories of extended cognition, the paper argues that enduring urban forms persist not only for symbolic or historical reasons, but because they embed structural properties conducive to resilience, legibility, social interaction, climatic adaptation, and human well-being. Recurring characteristics include fine-grained network connectivity, fractal scaling hierarchies, organized symmetry, articulated thresholds, and biophilic integration. Evidence from environmental psychology, public health, and urban morphology suggests that such properties correlate with reduced stress, increased walkability, stronger social capital, and improved ecological performance. The paper proposes a methodological framework—what we call the Adaptive Patterns Model—for identifying, evaluating, and translating this embedded intelligence into contemporary regeneration practice. The Model is presented as a four-phase, conceptually synthesized framework—integrating insights from complexity science and stigmergy, urban morphological analysis, and pattern-language methodology—comprising documentation, pattern extraction, encoding, and performance correlation. It concludes by challenging a still-prevalent assumption that contemporary conditions invalidate accumulated spatial knowledge. Instead, urban heritage is understood as adaptive capital within an ongoing evolutionary process, offering a structurally grounded foundation for resilient urban transformation. Full article

Global transition toward sustainable micro-mobility is an essential aspect of Saudi Vision 2030; however, high car dependency remains a significant barrier to public health and safety targets. In this context, this study explores the factors determining the adoption of electric bicycles (e-bikes) in Al-Qunfudhah, Saudi Arabia. The present research used a convenience sampling strategy through an online survey conducted via social media and texting, utilizing a designed questionnaire of 10 sections delivered to 171 participants, alongside a 5-point Likert scale. Additionally, the scientific validation and analysis were conducted utilizing internal consistency, validity and scale reliability via statistical analysis. The findings indicated a significant intention–action disparity; while respondents demonstrate a strong psychological intention to adopt e-bikes within 12 months (an average of 3.51), real household ownership was relatively low at 11.1%. In addition, a significant 71.9% of participants use private vehicles for short-distance travel (<5 km), influenced by an average bus stop distance of 21.22 km. The hierarchy of barriers indicates infrastructure and security as the main barrier, particularly the absence of dedicated bike lanes, and concerns regarding traffic safety. In contrast, a perception of physical fitness, and interpersonal interaction behave as significant facilitators. Public health data reveals an average weekly activity of 109.77 min, significantly lower than worldwide recommendations; however, 66.7% of individuals believe e-bikes may address the difference. The statistical evaluation acknowledged the questionnaire’s robustness, with significant Pearson correlation coefficients (p < 0.01) demonstrating internal consistency validity and Cronbach’s alpha values between 0.71 and 0.88 indicating high scale reliability, demonstrating a scientifically stable framework for assessing the measured behavioral determinants. The research recommends the establishment of shaded, dedicated micro-mobility networks and the enforcement of safety regulations to promote a healthy, multi-modal urban ecosystem. Full article

Classification methods such as exploratory factor analysis (EFA) and network community detection (NCD) are widely used to identify latent item groupings in multidimensional psychological assessments. However, direct comparisons between these approaches remain limited. In addition, evaluations of clustering methods often rely on overall classification metrics, which may obscure systematic differences in how well distinct types of items are recovered. Item characteristics—such as core–peripheral positions and loading patterns—may influence classification outcomes, yet few studies have examined how these item types interact with clustering methods. The present study addresses these gaps by comparing EFA and NCD within a unified machine-learning evaluation framework that varies sample size, latent structure, preprocessing strategy, and machine-learning classifier choice (Random Forests vs. Support Vector Machines). Results show that the performance of both EFA and NCD is influenced by sample size, item type, latent structure, and classifier choice. Moreover, the downstream classifier moderates how sensitive each method is to differences among item types. These findings highlight the importance of considering item-type heterogeneity when evaluating clustering methods and demonstrate the value of machine-learning-based frameworks for advancing psychometric classification approaches. Full article

Achieving high-quality employment for retired athletes is essential for promoting the holistic development of athletes and accelerating the construction of a strong sports nation. From the perspective of capital collaboration, this study develops a comprehensive analysis framework by incorporating human capital, social capital, and psychological capital to systematically investigate the influencing factors and configuration pathways for high-quality employment of retired athletes. Utilizing Necessary Condition Analysis (NCA) and fuzzy-set Qualitative Comparative Analysis (fsQCA), this study discovers three main findings. First, no single condition variable independently constitutes the necessary condition for high-quality employment. Second, three configuration pathways for achieving high-quality employment are identified, including human capital–social capital synergy, human capital–psychological capital synergy, and human capital–social capital–psychological capital integration. Third, vocational skill, as a component of human capital, emerges as an important condition in configurations associated with high-quality employment. Based on the findings, this research recommends improving the athlete security policy system, promoting the accumulation of human capital, strengthening the development of psychological capital, constructing diverse social support networks, and optimizing the pathways for retired athletes to achieve high-quality employment. These aims will support retired athletes in navigating career transitions effectively while securing stable and high-quality employment. Full article

Academic functioning in university students emerges from the interplay of motivational, self-regulatory, emotional, and contextual processes. The present study examined the network structure linking academic motivation, self-regulated learning, academic engagement, academic burnout, generalized anxiety, self-esteem, and students’ ratings of instruction. Participants were 530 university students from Western Romania (Mage = 28.86, SD = 9.75; 87.5% women). Data were collected through an online cross-sectional survey using validated self-report instruments. A Gaussian Graphical Model was estimated using the EBICglasso procedure to examine the unique associations among the study variables and their relative structural importance within the network. The results indicated a moderately dense psychological network, with academic burnout emerging as the most structurally central node. Intrinsic motivation toward achievement, identified regulation, and performance control were positioned within the adaptive core of the network, whereas burnout, anxiety, amotivation, and low self-esteem clustered within the maladaptive region. Academic engagement occupied an intermediary position linking motivational and self-regulatory processes. Overall, the findings support a systems-oriented interpretation of academic functioning, suggesting that burnout represents a key convergence point in students’ psychological functioning, while self-determined motivation and self-regulated learning may serve as protective processes. These results highlight the value of network analysis for identifying psychologically meaningful intervention targets in higher education. Full article

Background: This study examined age-related differences and interrelationships among psychological symptoms, personality traits, and emotional expression styles in a community sample of 151 participants aged 10–77 years, spanning four age groups: adolescents, young adults, middle-aged adults, and older adults. Methods: Psychological symptoms were assessed using the SCL-90, personality traits using the Big Five Inventory-2 (BFI-2), and emotional expression patterns were derived from facial expression recognition via a convolutional neural network (CNN) model. Kruskal–Wallis H tests were used to examine age-related differences. K-means cluster analysis was applied to identify emotional expression patterns, and logistic regression was used to construct a mental health risk screening model. Results: The young adult group (19–35 years) achieved the highest scores on the depression (M = 1.73) and anxiety (M = 1.61) dimensions, indicating a higher level of psychological distress during this life stage. Personality traits showed a significant developmental trajectory: neuroticism decreased with age (H(3) = 17.09, p < 0.001, η² = 0.11), declining from 2.69 in the young adult group to 2.17 in the older adult group; conscientiousness increased with age (H(3) = 37.39, p < 0.001, η² = 0.24), representing the most substantial age-related effect. K-means clustering identified three distinct emotional expression patterns: Cluster 1 was characterised by happiness, Cluster 2 by anger, disgust, and fear, and Cluster 3 by neutrality, sadness, and surprise. Cluster 2 exhibited the highest scores on neuroticism, anxiety, depression, and mood swings, and scored significantly higher than the other two clusters on interpersonal sensitivity, depression, anxiety, and hostility (p < 0.05). Mental health risk screening indicated that 26.5% of participants were classified as high-risk. Logistic regression analysis (AUC = 0.742) showed that neuroticism was the strongest predictor of elevated mental health risk (OR = 4.58), while extraversion (OR = 0.41) and conscientiousness (OR = 0.57) were significant protective factors. Conclusions: These findings provide exploratory evidence regarding age-related patterns of psychological symptoms and personality traits in a convenience sample and offer preliminary support for personality-based mental health risk screening. Notably, the SCL-90 was employed as a screening tool rather than for clinical diagnosis. Given the unequal age group sizes, particularly the small young adult subgroup, generalisability across the lifespan should not be assumed. Full article

Background/Objectives: Emotions can be recognized through external behavioral cues and internal physiological signals. Owing to the inherently complex psychological and physiological nature of emotions, models relying on a single modality often suffer from limited robustness. This study aims to improve emotion recognition performance by effectively integrating electroencephalogram (EEG) signals and facial expressions through a multimodal framework. Methods: We propose a multimodal emotion recognition model that employs a Multi-Scale Dilated Attention Convolution (MSDAC) network tailored for facial expression recognition, integrates an EEG emotion recognition method based on three-dimensional features, and adopts a self-learning decision-level fusion strategy. MSDAC incorporates Multi-Scale Dilated Convolutions and a Dual-Branch Attention (D-BA) module to capture discontinuous facial action units. For EEG processing, raw signals are converted into a multidimensional time–frequency–spatial representation to preserve temporal, spectral, and spatial information. To overcome the limitations of traditional stitching or fixed-weight fusion approaches, a self-learning weight fusion mechanism is introduced at the decision level to adaptively adjust modality contributions. Results: The facial analysis branch achieved average accuracies of 74.1% on FER2013, 99.69% on CK+, and 98.05% (valence)/96.15% (arousal) on DEAP. On the DEAP dataset, the complete multimodal model reached 98.66% accuracy for valence and 97.49% for arousal classification. Conclusions: The proposed framework enhances emotion recognition by improving facial feature extraction and enabling adaptive multimodal fusion, demonstrating the effectiveness of combining EEG and facial information for robust emotion analysis. Full article

Background: Nurse coaching can reduce stress throughout the complex psychosocial process associated with the cancer journey, which affects numerous spheres, such as neurological, psychological, physical, and emotional ones. The purpose of this paper is to review the literature to assess the extent of stress reduction among cancer care adopting nurse coaching interventions. Methods: This systematic review and meta-analysis was registered in PROSPERO with id no. CRD420261290368 and performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. The search approach was performed by mixing keywords with Boolean operators like “coaching intervention”, “oncology nursing” and “stress” throughout the CINAHL, Embase, PubMed, Scopus and Web of Science databases. We assessed stress levels alongside the nurse coaching interventions using the National Comprehensive Cancer Network (NCCN) Distress Thermometer. Results: A total of three studies were included, comprising 112 participants. Heterogeneity among studies was very high and statistically significant (p < 0.001; τ² = 1.02; I² = 96.2%, with a 95% CI: [85.89; 99.90]); thus, a random-effects model (REML) was applied. A small, non-significant reduction in stress levels following the nurse coaching intervention, with an overall SMD of −0.35 (SE = 0.60; 95% CI: [−1.52, 0.82]; p = 0.556), was recorded. Conclusions: Attention to standardization of core coaching components, while preserving flexibility and personalization, will be critical to advancing implementation in routine oncology care. Overall, nurse coaching represents a promising adjunct to comprehensive cancer care, with the potential to support psychological well-being, empower patients and caregivers, and address the broader determinants of stress throughout the cancer experience. Full article

Moving beyond traditional sociodemographic models, this study investigates the psychometric drivers of lexical change. Using Swiss German as a case study, we compare historical data from the Sprachatlas der deutschen Schweiz (1939–1958) with a recent large-scale app-based survey (N = 1013) to quantify trajectories over the past century. We identify four distinct mechanisms: exogenous convergence (Schmetterling), endo-normative leveling (Rande), endogenous innovation and divergence (schlittschuhlaufen), and diachronic persistence (Stäge). For the locally rooted speakers in our dataset, structural analysis indicates that traditional variables carry less weight than expected. While age remains the primary vertical predictor, psychological factors outperform traditional variables (e.g., gender, social networks) in this environment of ubiquitous exposure. Multivariate models demonstrate that lexical choices are strongly influenced by individual disposition: traits such as agreeableness accelerate the adoption of supraregional forms, whereas a strong local identity functions as a “brake” against standardization. Ultimately, while macro-factors create the pressure for change, individual micro-factors determine whether it takes hold. A speaker’s attitude acts as a “filter” and their personality as a “gate,” deciding whether they accept or resist new forms. These findings challenge purely structural accounts, suggesting that for these locally rooter speakers, even without high physical mobility, lexical change is shaped by a psychometric architecture. Full article

The analysis of brain data through electroencephalography (EEG) has become essential in neuroscience, affective computing, and brain–computer interfaces. Recent work associates EEG features with artificial neurotransmitter models, simulating emotions and rational–emotional decision-making using complexity theory. However, current methods face limitations: (1) linear temporal representations lacking memory and anticipation, (2) limited contextual adaptation, (3) difficulty with paradoxical affective states, and (4) absence of ethical reasoning in decision-making. We present a framework based on Sophimatics, using complex time ($t=t_{real}+i\cdot t_{imag}\in \mathbb{C}$) where $t_{real}$ represents chronology and $t_{imag}$ encodes experiential dimensions including memory depth and anticipatory imagination. The Super Time Cognitive Neural Network (STCNN) architecture enables the parallel processing of objective time sequences and subjective cognitive experiences. Our Sophimatics-assisted EEG analysis achieves: (1) two-dimensional temporal coherence integrating past experiences and future projections, (2) context-sensitive adaptation via ontological knowledge graphs, (3) interpretable symbolic reasoning compatible with clinical psychology, (4) mechanisms for resolving affective paradoxes, and (5) ethical constraints ensuring value-based decision-making. Across three case studies (emotion recognition, meditation-induced transitions, and brain–computer interface decision support), integrated Sophimatics models outperform traditional machine learning (15–22% accuracy improvement) and complexity theory models (8–14% improvement), while offering greater cognitive richness and immunity to incomplete data. Results establish a post-generative AI framework with computational wisdom: relationally interactive, ethically informed, and temporally consistent with human cognitive and affective life. The framework outlines paths toward next-generation neuromorphic systems achieving genuine understanding beyond pattern recognition. Full article

With the rapid development in new media and social platforms on the internet, some social hotspots or sensitive events can easily ferment and spread in the online space, attracting the attention or concentrated discussion of young students. Network cluster behavior is a collective behavior in which a large number of netizens collectively express and gather opinions around social hot issues of common concern, creating online public opinion. The study explored the influence of group psychology on the process of college students participating in online cluster behavior. A survey was conducted involving 2137 college students from over 10 universities in Zhejiang Province, Jiangsu Province, and other regions. The data were analyzed using correlation analysis and moderated mediation model testing. This study found that group psychological factors, such as emotional infection, depersonalization, the spiral of silence, relative deprivation, group polarization, and action mobilization, positively predicted network cluster behavior. The action mobilization of opinion leaders mediated the relationship between emotional infection and network cluster behavior. Group polarization mediated the relationship between the spiral of silence and network cluster behavior. Additionally, group efficacy moderated the latter part of the mediation process between group polarization and network cluster behavior. Full article