Search Results (197)

This paper investigates the existence of convergence clubs and asymmetric crisis dynamics across European Union and Western Balkan economies over the period 2004–2023. Using the Phillips–Sul log-t methodology, we reject overall income convergence and identify three distinct convergence clubs characterized by persistent structural heterogeneity. A multinomial logit framework reveals that institutional quality emerges as the strongest predictor of convergence club membership, indicating a clear governance hierarchy across European economies. We further estimate fixed-effects growth models with crisis interactions to examine asymmetric responses during major macroeconomic shocks (2008–2009, 2012–2013, and 2020–2021). Results indicate significant negative crisis effects, with middle-income club economies experiencing disproportionate contraction. These findings remain robust to Driscoll–Kraay corrections for cross-sectional dependence, alternative crisis definitions, and sigma-convergence analysis. Overall, the results suggest that European income dynamics are characterized not by uniform convergence, but by club-based adjustment patterns associated with institutional capacity and crisis resilience. The findings contribute to the literature on nonlinear convergence and institutional heterogeneity in integrated economic areas. Full article

Background: General Practice is facing workforce shortages across Europe. In Italy, the estimated shortage was 5575 General Practitioners in 2024, with high retirement rates and persistent difficulties in attracting new physicians to the profession. This study investigated postgraduate career intentions among medical students and factors associated with interest in General Practice. Methods: A cross-sectional survey using an anonymous 39-item questionnaire was conducted among fifth- and sixth-year medical students at the University of Milan. Associations were assessed using ordinal and multinomial logistic regression. Results: Among 359 respondents (response rate: 46.8%), only 2.2% identified General Practice as their preferred postgraduate career. Students considering General Practice attributed greater importance to community-based work (OR = 18.49, 95% CI 3.35–102.02) and professional autonomy (OR = 4.47, 95% CI 1.03–19.44). Previous community-based primary care experience was associated with choosing General Practice (OR = 32.43, 95% CI 3.85–273.50). Most students perceived theoretical and practical General Practice training as less adequate than that of other specialties, and only 25.1% considered their preparation sufficient to support career decision-making. Conclusions: Strengthening the academic integration of General Practice and meaningful community-based learning opportunities may contribute to more informed career choices while better preparing future physicians for healthcare systems increasingly centred on Primary Health Care. Full article

Background: In Gulf nations such as Saudi Arabia, where COVID-19 vaccination is optional, parental decision-making regarding vaccinating children is critical. Understanding the psychological factors that drive these decisions can shape future vaccination strategies. Methods: Between November 2021 and January 2022, a cross-sectional, web-based validated survey was conducted using chain-referral (snowball) sampling across multiple social media platforms. A total of 2176 parents residing in Saudi Arabia with children aged 5–18 years participated. The 5C psychological antecedents (confidence, complacency, constraints, calculation, and collective responsibility) were assessed. Multinomial logistic regression was employed to determine factors influencing parental decision-making across three child age groups: 5–11 years only, 12–18 years only, and both age ranges (5–18 years). Results: Overall, 77.9% of parents agreed to receive all scheduled doses of the COVID-19 vaccine themselves, while 58.6% agreed to vaccinate children aged 12 years or older, and only 41.9% agreed to vaccinate children younger than 12 years. Fathers were significantly more likely to accept vaccination [Odds ratio (OR) = 17.48, p = 0.01] compared to mothers, who showed higher reluctance (OR = 15.08, p = 0.01). Having a child aged 5–11 years increased reluctance threefold (OR = 3.16, p = 0.01). Parents who believed that returning to school was safe showed increased vaccine acceptance (OR = 4.32, p = 0.01). Confidence and collective responsibility were the strongest positive predictors of vaccine acceptance, particularly for younger children, with OR estimates of 1.23–1.53 for confidence and 1.33–2.13 for collective responsibility. Conversely, extensive information seeking (calculation) was associated with lower acceptance (OR = 0.09, p = 0.03). A history of COVID-19 hospitalization among family or friends was significantly associated with increased parental reluctance (p = 0.03). Conclusions: This study identifies key psychological and demographic factors influencing parental decisions regarding COVID-19 vaccination for children. Building parental confidence and fostering collective responsibility are essential for increasing vaccination rates, particularly among parents of younger children aged 5–11 years. Healthcare authorities should address vaccine safety concerns, provide clear and accurate information, and tailor strategies to parents who remain hesitant. Full article

We seek to identify the most relevant benefits offered by Medicare Advantage Health Plans that are attractive to members and that drive membership and market share. We explore plans operating in a single county in New Jersey between 2018 and 2023. A dataset of benefits from publicly available data sources was created and the variance inflation factor was applied to identify the correlation between the extracted features, avoiding multicollinearity and overparameterization problems. We categorized the variable market share and used it as a multinomial response variable with three categories: less than 0.3%, 0.3% to 1.5%, and over 1.5%. Categories were chosen to achieve approximately uniform distribution of plans (47, 60 and 65, respectively). A multinomial Lasso model using 5-fold cross validation tunes the penalty parameter and reduces overfitting by dropping some features from the model, thus increasing interpretability. For each category, important variables vary. Certain brands drive market share, as do PPO plans and prescription drug coverage. Benefits, particularly ancillary benefits that are not part of CMS’s required benefits, appear to have little influence, while financial terms such as deductibles, copays and out-of-pocket limits are associated with higher market share. Finally, we evaluated the multinomial Lasso model on a held-out test set. The model achieved an overall classification accuracy of 0.76, meaning that 76% of plans were correctly classified into the low, medium or high market share categories. Full article

Data-driven urban governance increasingly seeks to incorporate the needs of different population groups, yet child-sensitive evidence for public street-space renewal remains limited in older urban districts. Most studies still evaluate environmental conditions through population averages, with insufficient attention to heterogeneous child groups that may require differentiated planning responses. Based on an analytic sample of 314 children retained from 343 usable questionnaire responses collected from children aged 6–12 in the older urban districts of Xi’an, China, this study integrates street-activity characteristics and age- and sex-standardized body mass index (zBMI) using an established person-centered analytical approach. Latent Class Analysis (LCA) was used to identify children’s activity–health profiles, and multinomial logistic regression was used to examine associations between individual, family, and perceived street-environment factors and profile membership. Three profiles were identified: high-activity–healthy, high-intensity active, and low-activity–high-risk. The model-estimated low-activity–high-risk profile represented 35.7% of the analytic sample, and children assigned to this profile reported the lowest perceived safety and convenience. The findings suggest that profile-based analysis may inform child-sensitive street-renewal prioritization. Perceived safety and convenience showed the strongest and most consistent associations with membership in either the high-activity–healthy or high-intensity active profile relative to the low-activity–high-risk profile. These findings are associative and do not establish the effects of street interventions. The study therefore represents a context-specific extension and planning application of established analytical methods. Full article

This study examines correlates of preferred future work arrangements among adult residents of the Roanoke Metropolitan Statistical Area, Virginia, a mid-sized U.S. metropolitan region. It focuses on whether remote-work preference varies by sociodemographic characteristics, pandemic-era work-from-home experience, occupational perceptions, and attitudes toward physical interaction. Data were collected through a cross-sectional online survey administered in 2023, during the post-acute pandemic period. After excluding incomplete responses, duplicates, respondents younger than 18 years, cases without verifiable adult eligibility, and cases without verified residence in the study area, the final analytical sample included 636 respondents. Analyses included chi-square tests, Spearman rank correlations, a Stuart–Maxwell test of marginal homogeneity, and an adjusted multinomial logistic regression model. Among respondents with valid preference data in this nonprobability analytical sample, 63.8% preferred fully remote work. Among paired current–preferred cases, preferred arrangements differed significantly from current arrangements, with 87.0% of respondents reporting a directional change preferring movement toward a more remote arrangement. Preferred future work arrangement was significantly associated with age, education, earnings, marital status, housing tenure, perceived work-from-home efficiency, occupational remote-work perceptions, and physical-interaction importance; gender was not significant. Findings indicate substantial but socially differentiated remote-work preference, with implications for workforce planning in mid-sized metropolitan regions. Full article

Public responses during health crises are shaped by interacting risk perceptions, affect, trust, information needs, overload, misinformation, and protective behavior. Existing applications of the Risk Information Seeking and Processing (RISP) model are largely static and therefore cannot represent stochastic multichannel exposure, delayed correction, or policy feedback. We develop the Stochastic Probabilistic Dynamic RISP (SP-D-RISP) model, which recasts RISP as a bounded stochastic state-space system. Its symmetry structure is explicit: the channel-allocation mechanism is equivariant under simultaneous relabeling of channels and their parameter blocks, while the multi-agent dynamics are invariant to agent relabeling under exchangeable sampling and a label-independent policy. Channel-specific effects, heterogeneous traits, rumor shocks, and interventions generate symmetry breaking. The model combines softmax–multinomial channel competition, discounted Bayesian trust updating, and policy-coupled state transitions. Projection guarantees feasible states by construction, whereas stronger stochastic stability is conditional on a coefficient-level small-gain criterion. For the stationary bounded-memory specification, this criterion is sufficient for Wasserstein contraction, uniqueness of the invariant distribution, and geometric forgetting of initial conditions. The criterion is formulated at the coefficient level and is kept distinct from finite-horizon simulation diagnostics. For the fully disclosed semi-synthetic coefficient vector, the scenario-specific gain matrices have spectral radii between $0.852765$ and $0.857123$; the worst-case column-sum norm is $0.983948$. Thus, the fixed-policy kernels satisfy the stated contraction certificate. For deterministic time-varying paths, the calculation is used only as a common-path one-step certificate, and for the threshold-adaptive rule, it is used only mode by mode rather than as a stationary invariant-law claim. While concentration bounds and Monte Carlo inference quantify population and replication uncertainty, a semi-synthetic experiment with 2500 heterogeneous agents over 90 days examines trust and literacy heterogeneity, clarification delays, communication volume, and intervention portfolios. Within the calibrated SP-D-RISP scenarios, the simulations suggest that higher communication volume may reduce modeled protective behavior when overload effects dominate knowledge gains, delayed clarification may increase transient misinformation, and an integrated portfolio can yield a more favorable simulated outcome profile than the evaluated single-lever strategies. Full article

While drone delivery has gained significant scholarly and industrial interest, rural residents’ acceptance of these systems remains underexplored, despite the region’s acute logistics challenges and service inequities. Using survey data from rural U.S. residents, this study first estimates an ordered logistic regression (OLR) model to identify factors associated with five-level drone delivery acceptance. The study then compares OLR, multinomial logistic regression (MNL), Random Forest (RF), XGBoost, and LightGBM under matched feature sets to evaluate whether nonlinear machine-learning models improve prediction beyond the interpretable statistical baseline. Open-ended responses are coded into LLM-derived sentiment labels and added as supplementary predictors to test whether unstructured feedback improves acceptance prediction. Results show that willingness to pay is the strongest predictor of acceptance, while equitable same-day delivery demand and post-pandemic attitude adjustment are also positively associated with higher acceptance. Household disability status and urban accessibility are not significant after adjustment. In the five-level analysis, OLR provides a strong ordinal baseline, while XGBoost and other tree-based models improve selected class-level prediction metrics. In the binary high-acceptance analysis, machine-learning models show stronger predictive performance, especially when structured predictors are combined with sentiment features. This study contributes to rural drone-delivery literature by linking service equity, perceived value, and LLM-derived sentiment within a comparable statistical and machine-learning framework for rural service design. Full article

Background and Objectives: Overweight and obesity among medical students represent significant public health concerns as unhealthy weight status during medical training may affect future health and professional counseling practices. Understanding the factors associated with BMI categories may help inform future university-based health promotion strategies. This study aimed to assess the prevalence of overweight and obesity and examine sociodemographic, lifestyle, and dietary factors associated with BMI categories among medical students. Methods: This cross-sectional study was conducted among medical students at the College of Medicine, King Faisal University, between December 2025 and January 2026. Students were selected using proportionate stratified random sampling according to academic year and gender. Of the 422 students invited to participate, 382 students completed the study questionnaire and anthropometric assessment, yielding a response rate of 90.5%. Height and weight were measured in person using calibrated equipment. Data on demographic characteristics, lifestyle habits, dietary patterns, smoking, sleep, and physical activity were collected through a structured face-to-face questionnaire. Multinomial logistic regression analysis was performed to examine factors associated with BMI categories, using normal weight as the reference category. Results: A total of 382 medical students were included, with a median age of 21 years. The prevalence of overweight was 19.9%, and the prevalence of obesity was 14.1%, resulting in a combined overweight and obesity prevalence of 34.0%. In the adjusted multinomial logistic regression model, male gender was associated with being overweight compared with having normal weight (aRRR = 2.71, 95% CI: 1.22–5.98). Obesity showed associations with below-average financial status (aRRR = 13.53, 95% CI: 1.98–92.22), current smoking (aRRR = 17.00, 95% CI: 2.50–115.47), soft drink intake of 2–3 times/week (aRRR = 4.57, 95% CI: 1.13–18.49), and lower physical activity, including no activity (aRRR = 11.61, 95% CI: 2.07–65.01), rare activity (aRRR = 7.54, 95% CI: 2.21–25.80), and physical activity several times/month (aRRR = 5.86, 95% CI: 1.78–19.31). Always eating breakfast was associated with overweight (aRRR = 2.23, 95% CI: 1.05–4.73). Several adjusted estimates had wide confidence intervals, indicating limited precision. Conclusions: Overweight and obesity were common among medical students at King Faisal University and were associated with several sociodemographic, lifestyle, and dietary variables. Because of the cross-sectional design and the imprecision of some adjusted estimates, these findings should be interpreted as associations rather than causal relationships. Future longitudinal and multicenter studies using validated lifestyle measures are recommended to clarify temporal relationships and guide university-based health promotion strategies. Full article

Objective: This study aimed to evaluate the knowledge and clinical awareness of local anesthetic systemic toxicity (LAST), preventive practices, knowledge of lipid emulsion therapy, and institutional availability among dentists actively practicing in Türkiye, and to examine the demographic and professional factors associated with knowledge level. Methods: This descriptive cross-sectional study was conducted using a 15-item online questionnaire developed by the researchers. The analyses included 369 dentists actively practicing in Türkiye. Data were analyzed using descriptive statistics, the Kruskal–Wallis test, Dunn–Bonferroni pairwise comparisons, Spearman rank correlation, and multinomial logistic regression analysis. Results: Among the participants, 45.8% reported having basic knowledge of LAST, whereas only 2.7% reported detailed knowledge, including the management steps. Although 40.1% stated that they calculated the local anesthetic dose according to the patient’s body weight, only 3.3% reported preparing an emergency response plan for LAST, and 2.2% indicated that they were prepared to use treatment options such as lipid emulsion. Regarding lipid emulsion therapy, 59.1% of participants had low knowledge and 24.4% had superficial knowledge; only 0.5% reported detailed knowledge of the administration steps and dosing protocol. In terms of institutional availability, 45.0% did not know whether lipid emulsion was available at their institution, 40.9% reported that it was unavailable, and 14.1% reported that it was available. Knowledge levels differed according to professional status; however, the effect size was small (H(2) = 13.129; p = 0.001; ε² = 0.030). No statistically significant association was found between years of professional experience and knowledge level (ρ = 0.020; p = 0.702). Conclusions: Although dentists’ self-reported awareness of LAST varied, detailed knowledge of the administration steps and dosing protocol for lipid emulsion therapy, as well as institutional preparedness, remained limited. The findings suggest that strengthening practice-oriented education on the prevention and management of LAST and reviewing lipid emulsion availability and emergency response protocols in clinical institutions may be beneficial. Full article

Dengue virus (DENV) and chikungunya virus (CHIKV) co-circulate in many regions and present with overlapping clinical features, which complicate accurate diagnosis and disease management. This study develops an integrative transcriptomic framework to identify robust host gene signatures that distinguish between dengue, chikungunya, and healthy states. Publicly available RNA sequencing (RNA-seq) datasets derived from human blood samples were analyzed using a cross-validation design to ensure robustness and prevent information leakage. Differential expression analysis was performed independently within each dataset using the Generalized Linear Models with Quasi-Likelihood F-tests and Magnitude–Altitude Scoring (GLMQL-MAS) framework, followed by Cross-Magnitude–Altitude Scoring (Cross-MAS) integration to identify shared and virus-specific gene signatures. A strict consensus approach across folds was applied to derive reproducible gene sets. These signatures were used for dimensionality reduction and multinomial logistic regression to evaluate classification performance. A small subset of selected genes showed strong discriminative performance within the cross-validation framework, with test balanced accuracy reaching 0.97, which improved upon models using all genes. Biologically, both infections exhibited a shared antiviral response characterized by interferon signaling and innate immune activation. However, distinct virus-specific patterns were identified. Dengue infection was associated with cell-cycle and DNA replication pathways, while chikungunya infection showed stronger enrichment of inflammatory and immune signaling pathways, including NF-kappaB and Toll-like receptor signaling. Overall, this study provides a cross-validation-based framework for integrative transcriptomic analysis and identifies compact, reproducible host-response signatures with strong discriminative signals in the analyzed cohorts. These signatures require validation in larger independent cohorts before any clinical or diagnostic application. Full article

Background/Objectives: Children of families facing unmet social needs experience higher rates of adverse outcomes compared to those not experiencing unmet social needs. This study aimed to identify factors associated with families’ unmet social needs as reported by parents or guardians at their children’s primary care visits. Methods: This cross-sectional study recruited English-speaking primary caregivers of children less than 18 years of age from the Southwestern Ohio Ambulatory Research Network (SOAR-Net) who were surveyed between January 2023 and August 2024. Surveys included the Maternal Social Support Index, Social Capital Scale, RAND Depression Screener, Children with Special Health Care Needs Screener, Medical Expenses of Children Survey, a 10-item social needs screener, and demographics. Data were analyzed with chi-square or Fisher’s exact tests, adjusted logistic regression, and ANOVA. Results: Among 1167 respondents (78% response rate), 1114 provided complete data. Primary caregivers were predominantly mothers (79.9%) or fathers (13.6%), White (72.0%) or Black (16.0%), and had an associate’s degree or less (65.1%). The mean (SD) index child’s age was 6.4 (5.3) years, and 52.4% were female. Underinsurance, positive depression screens, and poor child health were positively associated with unmet social needs. Higher scores for social support and social capital were associated with fewer social needs. Multinomial logistic regression revealed significant relationships with reporting two or more unmet social needs with the following variables: childhood underinsurance, household annual income < $50,000, positive depression screens, raising a child with a chronic health condition, and Black race/ethnicity. Conclusions: Several significant social factors were independently associated with a greater number of unmet social needs. These findings highlight the complex interplay among social factors in children’s healthcare. Future research should explore the putative longitudinal stability of these relationships. Full article

Background: Traumatic brain injury (TBI) and substance use disorder (SUD) frequently co-occur due to shared risk factors and a potentially bidirectional relationship. However, epidemiological patterns in rural populations remain understudied despite known disparities in access and outcomes. This study aimed to characterize the relationship between TBI and SUD in a rural Southwestern population, including demographic and clinical patterns of diagnostic sequencing. Methods: A retrospective observational study was conducted using electronic health records and trauma registry data (2022–2023) from a rural trauma center. Cohort one included 24,389 emergency department encounters with ICD-10 codes for TBI or SUD. Cohort two included 248 trauma registry patients with TBI and SUD diagnoses. Descriptive statistics and multinomial logistic regression models were used to evaluate diagnostic patterns and associated demographic factors. Results: Males were more likely to have co-occurring TBI and SUD (Relative Risk Ratio [RRR] = 1.35), while increasing age was associated with TBI-only diagnoses. Among patients with multiple visits and diagnoses, 16% had co-diagnoses, while 9% had sequential diagnoses. American Indian/Alaska Native patients had higher co-diagnosis risk compared to White patients (RRR = 2.21, p < 0.001). Higher blood alcohol concentration was associated with lower Glasgow Coma Scale scores (r = −0.15, p = 0.022), indicating greater severity. Conclusions: TBI and SUD frequently co-occur in rural populations, with notable disparities by sex and race/ethnicity. Emergency Departments are critical points of care for interventions such as screening for both substance use and head injury when either is suspected, and employing culturally responsive education and referral pathways upon discharge. Full article

The parameter space in a regression model for multinomial time series data contains the regression parameters those explain the effects of the time dependent covariates, and the dynamic dependence or category transition parameters those explain the influence of the past responses on the multinomial response at a given time. The estimation of the regression parameters can be negatively affected when higher dimension of the parameter space is considered specially for the transition parameters. In this paper we propose a parameter dimension-split approach where a conditional generalized quasi-likelihood (CGQL) estimating function is first developed for the dynamic dependence parameters in terms of unknown regression parameters which is exploited in the next step to develop an observed information matrix based maximum likelihood (ML) estimating equation for the main regression parameters. More specifically, this split approach helps to write the actual joint likelihood function of regression and dynamic dependence parameters as a likelihood function of regression parameters only by replacing the dynamic dependence parameters with their CGQL estimates obtained in the first step. As the time series length is generally large in practice, we have made sure that the proposed CGQL and ML estimators are asymptotically reliable, that is consistent for the respective parameters. Full article

Introduction: Advances in pediatric oncology have transformed cancer into a condition with chronic and long-term developmental consequences. While survival rates have improved significantly, the literature on psychosocial outcomes remains fragmented and inconsistent, with a notable lack of person-centered analyses that account for the heterogeneity of adaptive trajectories. Current evidence fails to explain why survivors with similar clinical profiles exhibit divergent psychological phenotypes, particularly regarding the late effects of multimodal treatments. The aim of this study was to identify heterogeneous psychosocial profiles among adolescent cancer survivors and to examine their associations with treatment complexity and quality of life. Materials and Methods: This cross-sectional study included 165 adolescents aged 12–18 years (mean age: 14.64 years) who were in clinical remission following oncological treatment. Standardized assessment tools were used: the Children’s Depression Inventory 2 (CDI-2™) to measure depressive symptoms, the KIDSCREEN-10 index to assess health-related quality of life (HRQoL), and a scale evaluating satisfaction across 14 life domains. Adaptive profiles were identified using a Two-Stage Cluster Procedure, and risk factors were examined using multinomial logistic regression. Results: Four clusters were identified in the study population: a depressive–dysphoric profile, an anhedonic-withdrawn profile, a highly adaptive profile, and a mixed (struggling) profile. Treatment complexity was identified as a significant independent predictor of membership in the high-distress (depressive) cluster. While each additional therapeutic modality beyond standard chemotherapy was associated with a markedly increased risk (OR = 8.91; p < 0.001), the relatively wide confidence interval (95% CI: 3.27–24.31) suggests that the exact magnitude of this effect should be interpreted with caution. The high lower bound of the interval (3.27), however, strongly supports the directional association of cumulative iatrogenic burden with psychological adaptation. Subjective quality of life functioned as a protective factor against depressive symptoms (OR = 0.57); however, paradoxically, higher self-reported quality of life increased the likelihood of classification into the anhedonic group (OR = 1.81). This divergence between high self-reported HRQoL and social withdrawal potentially suggests a ‘well-being paradox’. It is hypothesized that standard HRQoL instruments may primarily capture physical remission and relief from acute somatic symptoms, potentially masking underlying social–emotional deficits. This suggests that HRQoL scores in survivors should be interpreted with caution and complemented by specific affective screenings. Conclusions: The absence of a uniform pattern of psychological response to cancer among adolescent survivors supports the validity of a patient-centered approach. The burden associated with intensive multimodal treatment significantly increases the likelihood of full-syndrome depression during adolescence. Moreover, the identification of a cluster suggestive of anhedonic and socially withdrawn features highlights the limitations of standard screening tools focused solely on the detection of overt sadness. This heterogeneity underscores the need for personalized psycho-oncological care and the implementation of intensified monitoring for patients at high medical risk. Full article