Search Results (338)

According to the literature, the advancement of information and communication technology (ICT) has increased individual exposure to scams, turning fraud victimization into a significant concern. While prior research has primarily focused on socio-demographic predictors of fraud victimization, this study adopts a behavioral perspective that is grounded in the Signal Detection Theory (SDT) to investigate the likelihood determinants of individuals becoming fraud victims. Using survey data of 671 Indonesian respondents analyzed with the Partial Least Squares Structural Equation Modeling (PLS-SEM), we explored the roles of vigilance and financial literacy in moderating the relationship between fraud exposure and victimization. Our findings substantiate the notion that higher exposure to fraudulent activity significantly increases the likelihood of victimization. The results also show that vigilance negatively moderates the relationship between fraud exposure and fraud victimization, suggesting that individuals with higher vigilance are better at identifying scams, thereby decreasing their likelihood of becoming fraud victims. Furthermore, financial literacy is positively related to vigilance, indicating that financially literate individuals are more aware of potential scams. However, the predictive power of financial literacy on vigilance is relatively low. Hence, while literacy helps a person sharpen their indicators for detecting fraud, psychological, behavioral, and contextual factors may also affect their vigilance and decision-making. Full article

Despite growing recognition of the role that social determinants of health (SDOHs) and health-related social needs (HRSNs) play in chronic disease, limited research has examined their associations with Chronic Obstructive Pulmonary Disease (COPD) in population-based studies. This cross-sectional study analyzed 2022 Behavioral Risk Factor Surveillance System (BRFSS) data from 37 U.S. states and territories to determine how financial hardship, food insecurity, employment loss, healthcare access barriers, and psychosocial stressors influence the prevalence of COPD. Weighted logistic regression models were used to assess the associations between COPD and specific SDOHs and HRSNs. Several individual SDOH and HRSN factors were significantly associated with COPD prevalence, with financial strain emerging as a particularly strong predictor. In models examining specific SDOH factors, economic hardships like inability to afford medical care were strongly linked to higher COPD odds. Psychosocial HRSN risks, such as experiencing mental stress, also showed moderate associations with increased COPD prevalence. These findings suggest that addressing both structural and individual-level social risks may be critical for reducing the prevalence of COPD in populations experiencing financial challenges. Full article

Anxiety is a significant mental health concern in universities worldwide. This study examines the structure of anxiety symptoms and their relationship with contextual stressors among pre-service English teachers. The sample included 269 students enrolled in a Teaching English as a Foreign Language program at a public university in Manabí, Ecuador. Data were collected using the Zung Self-Rating Anxiety Scale and a custom-designed questionnaire identifying anxiety triggers. Results showed that while most students reported normal or mild anxiety levels, a considerable portion exhibited moderate to severe symptoms. Cluster analysis revealed three emotional profiles, with the high-anxiety group strongly associated with stressors such as economic hardship and job insecurity. Academic pressure and financial instability emerged as the strongest predictors of anxiety. These findings highlight the urgent need to develop and evaluate targeted psycho-educational strategies to prevent and reduce anxiety within teacher training programs in higher education. Full article

Background/Objectives: Given the heterogeneous nature of long COVID, its treatment and management remain challenging. This study aimed to investigate whether poor pre-pandemic sleep quality, its deterioration during the peak of the pandemic, and circadian preference increase the risk of long COVID symptoms. Methods: An online survey was conducted between 9 October and 12 December 2022, with 384 participants who had recovered from COVID-19 at least three months prior to data collection. Participants were categorized based on the presence of at least one long COVID symptom. Logistic regression models assessed associations between sleep-related variables and long COVID symptoms. Results: Participants with long COVID symptoms reported significantly poorer sleep quality, higher perceived stress, greater somatic and cognitive pre-sleep arousal, and elevated levels of post-traumatic stress symptoms, anxiety, depression, and aggression. Fatigue (39.8%) and memory problems (37.0%) were the most common long COVID symptoms. Sleep deterioration during the pandemic peak was reported by 34.6% of respondents. Pre-pandemic poor sleep quality, its deterioration during the pandemic, and poor sleep at the time of the survey were all significantly associated with long COVID. An extreme morning chronotype consistently predicted long COVID symptoms across all models, while an extreme evening chronotype was predictive only when accounting for sleep quality changes during the pandemic. COVID-19 frequency, severity, financial impact, and somatic pre-sleep arousal were significant predictors in all models. Conclusions: Poor sleep quality before the pandemic and its worsening during the pandemic peak are associated with a higher likelihood of long COVID symptoms. These findings underscore the need to monitor sleep health during pandemics and similar global events to help identify at-risk individuals and mitigate long-term health consequences, with important clinical and societal implications. Full article

The main objective of this study is to analyse the relevance of financial performance as an accurate predictor of ESG scores for French companies from 2010 to 2022. To this end, Machine Learning techniques such as linear regression, polynomial regression, Random Forest, and Support Vector Regression (SVR) were employed to provide more accurate and reliable assessments, thus informing the ESG rating attribution process. The results obtained highlight the excellent performance of the Random Forest method in predicting ESG scores from company financial variables. In addition, the approach identified specific financial variables (operating income, market capitalisation, enterprise value, etc.) that act as powerful predictors of companies’ ESG scores. This modelling approach offers a robust tool for predicting companies’ ESG scores from financial data, which can be valuable for investors and decision-makers wishing to assess and understand the impact of financial variables on corporate sustainability. Also, this allows sustainability investors to diversify their portfolios by including companies that are not currently rated by ESG rating agencies, that do not produce sustainability reports, as well as newly listed companies. It also gives companies the opportunity to identify areas where improvements are needed to enhance their ESG performance. Finally, it facilitates access to ESG ratings for interested external stakeholders. Our study focuses on using advances in artificial intelligence, exploring machine learning techniques to develop a reliable predictive model of ESG scores, which is proving to be an original and promising area of research. Full article

Background and Objectives: Orthodontic treatment aims to enhance dental aesthetics and function, yet many patients report dissatisfaction. This study was designed with the following objectives: To assess overall patient satisfaction during active orthodontic treatment; to identify key psychosocial and clinical predictors of satisfaction, including self-confidence, social experiences, and cost perception; to evaluate the impact of orthodontist–patient communication on satisfaction and perceived treatment outcomes; to explore the relationship between aesthetic improvement and willingness to undergo treatment again. Materials and Methods: A cross-sectional survey was conducted using structured questionnaires to assess satisfaction, pain perception, treatment expectations, and communication quality. Statistical analyses, including correlations and regression models, were used to identify predictors of satisfaction. The study included 450 orthodontic patients from the north-eastern region of Romania, undergoing active treatment at the time of data collection. Results: The strongest predictor of satisfaction was improved self-confidence and smile aesthetics (r = 0.62). Effective communication with orthodontists significantly increased satisfaction (r = 0.58, p = 0.002), while perceived high costs had a negative impact (r = −0.41). Pain and discomfort were common, with 90% of patients experiencing treatment-related pain, leading to reduced compliance. Social embarrassment due to braces also contributed to dissatisfaction (r = −0.47). Conclusions: Patient satisfaction with orthodontic treatment is primarily influenced by aesthetic improvements and effective communication. While enhanced smile perception boosts confidence, financial concerns and social discomfort may negatively affect the overall experience. Improving accessibility to treatment and providing comprehensive patient support are essential for optimizing patient satisfaction. Full article

This research investigates the liability management of Thai life insurers in a prolonged low-interest rate environment. It examines the impact of interest rate changes on life insurance products, solvency, and profitability. The study identifies a significant shift in product portfolios toward non-interest-sensitive products, which helps mitigate financial risk and enhance solvency. The solvency of Thai life insurers is influenced by their return on assets, with higher risk exposures requiring more capital, potentially lowering solvency levels. However, the proportion of risky investment assets is not significantly related to the solvency position in the Thai market. The market index return is a significant predictor of stock returns for Thai life insurers, while changes in interest rate sensitivity are not statistically significant between low-rate and normal periods. The average solvency level under Thailand’s regulatory regime is also not statistically different between normal and prolonged low-interest rate situations. This study contributes to the understanding of liability management practices among life insurers in Thailand and provides insights into the challenges and strategies for maintaining solvency and profitability in a low-interest rate environment. Full article

Background/Objectives: Childhood stunting remains a critical public health issue in low- and middle-income countries. Despite Malaysia’s economic growth, there is limited large-scale evidence on the determinants of stunting among children from infancy to primary school age. This cross-sectional study, part of South East Asian Nutrition Surveys II (SEANUTS II), aimed to determine sociodemographic and environmental risk factors for stunting among 2989 children aged 0.5–12 years. Methods: Children were recruited from four regions in Peninsular Malaysia (Central, East Coast, 2022–2030Northern, Southern). Standing height or recumbent length was measured, and stunting was classified based on WHO criteria (height-for-age Z-score below −2 standard deviations). Parents reported information on socioeconomic status, sanitation facilities, and hygiene practices. Multivariate binary logistic regression was used to determine the determinants of stunting. Results: Stunting prevalence was 8.9%, with infants (aOR = 2.92, 95%CI:1.14–7.52) and young children (aOR = 2.92, 95%CI:1.80–4.76) having higher odds than school-aged children. Key biological predictors included low birth weight (aOR = 2.41; 95%CI:1.40–4.13) and maternal height <150 cm (aOR = 2.24; 95%CI:1.36–3.70). Chinese (aOR = 0.56; 95%CI:0.35–0.88) and Indian children (aOR = 0.16; 95%CI:0.05–0.52) had a lower risk of stunting compared to Malays. Conclusions: This study highlights the ongoing challenge of childhood stunting in Malaysia, with age, birth weight, ethnicity, and maternal height identified as key determinants. These findings call for early identification of at-risk households and targeted support, especially through education and financial aid to foster healthy child growth. Full article

Background: The Kidney Disease and Quality of Life Short Form (KDQOL-SF™ 1.3) is widely used to assess health-related quality of life (HRQoL) in patients with end-stage renal disease. However, no prior validation had been conducted in Kazakhstan, where both Kazakh and Russian are commonly spoken. This study aimed to validate the Kazakh and Russian versions of the KDQOL-SF™ 1.3 and to identify predictors of HRQoL among hemodialysis patients in Kazakhstan. Methods: A cross-sectional survey was conducted among 217 adult hemodialysis patients from February to April 2025 using a mixed-methods approach (in-person interviews and online data collection). Psychometric testing included Cronbach’s alpha, floor and ceiling effect analysis, and Pearson correlations with self-rated overall health. Multiple linear regression was used to identify predictors of the Kidney Disease Component Summary (KDCS), Physical Component Summary (PCS), and Mental Component Summary (MCS) scores. Results: Both language versions demonstrated acceptable to excellent internal consistency (Cronbach’s α = 0.692–0.939). Most subscales were significantly correlated with self-rated health, supporting construct validity. Regression analyses revealed that greater satisfaction with care, better economic well-being, and more positive dialysis experiences were significant predictors of higher KDCS and MCS scores. Lower PCS scores were associated with female gender, comorbidities, and financial burden. Importantly, financial hardship and access challenges emerged as strong negative influences on HRQoL, underscoring the role of socioeconomic and care-related factors in patient well-being. Conclusions: The KDQOL-SF™ 1.3 is a valid and reliable tool for assessing quality of life among Kazakh- and Russian-speaking hemodialysis patients in Kazakhstan. Integrating this instrument into routine clinical practice may facilitate more personalized, patient-centered care and help monitor outcomes beyond traditional clinical indicators. Addressing economic and access-related barriers has the potential to significantly improve both physical and mental health outcomes in this vulnerable population. Full article

Background: Employee turnover poses a multi-faceted challenge to organizations by undermining productivity, morale, and financial stability while rendering recruitment, onboarding, and training investments wasteful. Traditional machine learning approaches often struggle with class imbalance and lack transparency, limiting actionable insights. This study introduces an Explainable AI (XAI) framework to achieve both high predictive accuracy and interpretability in turnover forecasting. Methods: Two publicly available HR datasets (IBM HR Analytics, Kaggle HR Analytics) were preprocessed with label encoding and MinMax scaling. Class imbalance was addressed via GAN-based synthetic data generation. A three-layer Transformer encoder performed binary classification, and SHapley Additive exPlanations (SHAP) analysis provided both global and local feature attributions. Model performance was evaluated using accuracy, precision, recall, F1 score, and ROC AUC metrics. Results: On the IBM dataset, the Generative Adversarial Network (GAN) Transformer model achieved 92.00% accuracy, 96.67% precision, 87.00% recall, 91.58% F1, and 96.32% ROC AUC. On the Kaggle dataset, it reached 96.95% accuracy, 97.28% precision, 96.60% recall, 96.94% F1, and 99.15% ROC AUC, substantially outperforming classical resampling methods (ROS, SMOTE, ADASYN) and recent literature benchmarks. SHAP explanations highlighted JobSatisfaction, Age, and YearsWithCurrManager as top predictors in IBM and number project, satisfaction level, and time spend company in Kaggle. Conclusion: The proposed GAN Transformer SHAP pipeline delivers state-of-the-art turnover prediction while furnishing transparent, actionable insights for HR decision-makers. Future work should validate generalizability across diverse industries and develop lightweight, real-time implementations. Full article

Technology is essential in higher education, yet disparities in access disproportionately affect first-generation college students. This study examines how technology access and financial stress impact academic performance for first-generation (FGCS) and continuing-generation college students (CGCS). Students (N = 430) were asked to reflect on their experiences during the COVID-19 pandemic, particularly their technology access and financial stress. Results showed that FGCS reported significantly lower technology access and higher levels of financial stress than CGCS. Greater technology access was a significant positive predictor of academic performance for FGCS but not CGCS. However, this effect diminished when financial stress was added to the regression model. Moderation analysis showed that financial stress significantly moderated the relation between technology access and academic performance. This suggests that under high financial stress, technology access becomes a critical resource for academic performance. Full article

Electricity theft remains a persistent challenge, particularly in developing economies where infrastructure limitations and socioeconomic disparities contribute to illegal connections. This study analyzes the determinants influencing electricity theft in Kinshasa, the Democratic Republic of Congo, using a logistic regression model applied to 385 observations, which includes random bootstrapping sampling for enhanced stability and power analysis validation to confirm the adequacy of the sample size. The model achieved an AUC of 0.86, demonstrating strong discriminatory power, while the Hosmer–Lemeshow test (p = 0.471) confirmed its robust fit. Our findings indicate that electricity supply quality, financial stress, tampering awareness, and billing transparency are key predictors of theft likelihood. Households experiencing unreliable service and economic hardship showed higher theft probability, while those receiving regular invoices and alternative legal energy solutions exhibited lower risk. Lasso regression was implemented to refine predictor selection, ensuring model efficiency. Based on these insights, a multifaceted policy approach—including grid modernization, prepaid billing systems, awareness campaigns, and regulatory enforcement—is recommended to mitigate electricity theft and promote sustainable energy access in urban environments. Full article

Breast cancer (BC) is the leading cancer diagnosis among women in Puerto Rico. Psychological distress is prevalent in this population, and social determinants may exacerbate this risk. This study examines whether sociodemographic characteristics, financial burden, and social support levels are associated with symptoms of anxiety and depression in Puerto Rican women with BC. A quantitative secondary analysis was conducted on a sample of 208 Hispanic women with BC, utilizing the Patient Health Questionnaire (PHQ-8) and the Generalized Anxiety Disorder (GAD-7) questionnaire. These scores were compared with sociodemographic values and Interpersonal Support Evaluation List (ISEL-12) scores, establishing statistical significance through association, parametric, and non-parametric tests, and regression models. 38.5% and 26.4% of participants showed clinically significant symptoms of depression and anxiety, respectively. Age and perceived income showed significant associations with psychological outcomes. However, regression analysis revealed perceived income as the only significant predictor for both depression and anxiety. Tangible and belonging support were significantly lower in participants with symptoms of depression, while appraisal support was significantly lower in participants with symptoms of anxiety. Findings highlight the influence of perceived financial stress on mental health and the need for psychosocial interventions tailored to the patients’ economic context. Full article

California’s SB 1014 (Clean Miles Standard) mandates ridehailing fleet electrification to reduce emissions from vehicle miles traveled, posing financial and infrastructure challenges for drivers. This study employs a mixed-methods approach, including expert interviews (n = 10), group discussions (n = 8), and a survey of full- and part-time drivers (n = 436), to examine electric vehicle (EV) adoption attitudes and policy preferences. Access to home charging and prior EV experience emerged as the most statistically significant predictors of EV acquisition. Socio-demographic variables, particularly income and age, could also influence the EV choice and sensitivity to policy design. Full-time drivers, though confident in the EV range, were concerned about income loss from the charging downtime and access to urban fast chargers. They showed a greater interest in EVs than part-time drivers and favored an income-based instant rebate at the point of sale. In contrast, part-time drivers showed greater hesitancy and were more responsive to vehicle purchase discounts (price reductions or instant rebates at the point of sale available to all customers) and charging credits (monetary incentive or prepaid allowance to offset the cost of EV charging equipment). Policymakers might target low-income full-time drivers with greater price reductions and offer charging credits (USD 500 to USD 1500) to part-time drivers needing operational and infrastructure support. Full article

Background: Work engagement is essential to the well-being of nurses and the quality of health care, particularly in high-demand urban hospital environments in Bangkok. To determine the levels of work engagement—vigor, dedication, and absorption—among nurses in a Thai urban tertiary hospital, and to identify associated demographic and occupational predictors. Materials and Methods: A cross-sectional study was conducted among 650 nurses at a tertiary university hospital in Bangkok, Thailand, from February to March 2025. Participants were selected through simple random sampling. They completed an online survey including demographic data and the Utrecht Work Engagement Scale (UWES), which assesses three dimensions of engagement: vigor, dedication, and absorption. To identify the predictors of high engagement levels, chi-square tests and multivariate binary logistic regression were used. Results: Most nurses reported low engagement across all dimensions: 73.1% for vigor, 69.1% for dedication, and 70.0% for absorption. In the adjusted models, monthly income was a significant predictor of higher vigor and dedication, whereas no significant predictors emerged for absorption. Other variables, including age, experience, and professional rank, were significant in the bivariate analyses but not in the multivariate models. Conclusions: Nurse engagement remains suboptimal in the urban tertiary hospital setting, with financial compensation emerging as a key determinant. Strategic interventions to improve income equity and career development may help enhance engagement and retention in the nursing workforce. Full article