Search Results (44)

In everyday life, individuals frequently encounter situations in which they may join new groups; however, previous research has primarily focused on issues that arise after group formation, leaving insufficient attention to the processes preceding spontaneous group affiliation. This study investigates how different levels of group competence influence individuals’ willingness to join, using Optimal Distinctiveness Theory as its theoretical framework. Through two experiments, it systematically examines participants’ willingness to join groups of varying competence levels and how this willingness is moderated by participants’ own competence. The results indicate that high-competence groups demonstrate stronger member attractiveness and effectively promote individuals’ willingness to join. Crucially, the group’s attraction to individuals is moderated by individuals’ own competence. When group competence is lower than one’s own competence level, willingness to join increases as group competence rises toward one’s own level. Strikingly, when group competence exceeds one’s own, willingness to join remains uniformly high and stable, rather than decreasing as Optimal Distinctiveness Theory would predict. These findings suggest that individuals engage in a psychological trade-off based on competence alignment when autonomously choosing whether to join social or professional groups. We interpret this pattern as evidence for a “downward aversion, upward assimilation” heuristic in group affiliation decisions. The present research also has implications for understanding how groups can strategically manage their reputation to attract prospective members, as well as how individuals make group-joining decisions at different stages of career development. Full article

Background/Objectives: We aimed to determine risk factors and to design a clinically based predictive model for a failed vacuum assisted delivery (VAD). Methods: We conducted a retrospective cohort study in a single tertiary university-affiliated medical center between 2011 and 2023. The study population consisted of singleton pregnancies with a VAD trial. The study group comprised cases of a failed VAD, defined as the occurrence of any of the following: (1) more than two vacuum cup detachments; (2) extraction duration exceeding 20 min; or (3) abandonment of the vacuum attempt by the operating physician, with conversion to urgent cesarean delivery (CD). The control group comprised cases of successful VADs. Factors associated with failed VAD were examined by univariate and multivariate analyses. A prediction score was developed to predict failed VAD. A receiver-operating characteristic curve (ROC) was utilized for the model. Internal validation was performed by means of a 70/30 train–test split, with model performance evaluated on the validation set using ROC analysis. Results: A total of 131,019 women delivered in our center during the study period. VAD was attempted in 8885 (6.8%) cases, of which 172 (1.9%) failed trials that led to urgent CDs. Several risk factors for a failed VAD were identified, including induction of labor, fetal head station below +2 cm relative to the ischial spines, duration of the second stage of delivery >3.5 h, preeclampsia, birthweight >3750 g, and male gender. The prediction score demonstrated good discriminatory performance, with an AUC of 0.723 (95% CI 0.637–0.810). Internal validation using a 30% holdout cohort revealed that the model maintained good performance, with an AUC of 0.764 (95% CI 0.619–0.909; p < 0.001). Conclusions: Our model has the potential to assist obstetricians with VAD decision-making and parturient counseling, as well as identifying parturients at high risk for complicated deliveries. Full article

Background: Artificial intelligence (AI) is increasingly being integrated into healthcare, with applications ranging from predictive analytics to clinical decision support. In clinical nutrition, AI tools offer opportunities to improve workflow efficiency, enhance dietary assessment, and personalize nutrition care. Despite growing interest, little is known about registered dietitian nutritionists’ (RDNs) perceptions of AI in clinical practice. The aim of the present study was to assess RDNs’ attitudes toward AI use within a large healthcare system, along with their perceived barriers in this regard. Methods: A cross-sectional survey was developed through expert review and distributed electronically via REDCap to RDNs across Mayo Clinic’s academic campuses and affiliated health system sites. The 23-item survey included Likert-scale items addressing AI’s potential utilization within clinical care, perceived benefits and risks, and readiness for adoption. Responses were summarized using descriptive statistics. Factor analysis identified underlying constructs related to AI attitudes. Differences stratified by age and years of experience were evaluated using ANOVA. Results: Of the 185 RDNs invited, 64 (35%) responded. Two factors emerged: optimism regarding AI usage (Cronbach’s α = 0.94) and skepticism about implementation (α = 0.76). The overall mean ± SD score for optimism was 0.1 ± 0.6 (neutral), while skepticism averaged 1.0 ± 0.6 (moderate). Skepticism differed by years of experience (p = 0.012), with the lowest levels observed among RDNs with ≥21 years of practice. No significant differences were observed across age groups. Discussion: RDNs demonstrated neutral attitudes toward AI use but expressed concerns about accuracy, training, and implementation challenges. Addressing these concerns through education and structured implementation strategies may facilitate successful adoption of AI in dietetic practice. Full article

In Peru, where socioeconomic inequalities remain a critical challenge, responsible consumption has shifted from individual decisions to organizational strategies with the potential to reduce structural disparities. This study adopted an exploratory–descriptive qualitative design to gain an in-depth understanding of organizational initiatives for responsible consumption and their contributions to social equity. Using documentary analysis, the READ protocol (Read, Extract, Analyze, Distill) was applied to systematically examine public information from 104 Perú Sustainable-affiliated organizations across 16 economic sectors. The analysis identified six categories of initiatives: eco-efficient management, circular economy, sustainable supply chains, education and awareness, sustainable products, and green financing that are linked to five dimensions of equity: economic inclusion, access to essential services, gender equality, inclusion of vulnerable populations, and capacity building. The circular economy (54.8%) and sustainable supply chains stood out for their greater potential to include vulnerable groups by integrating them into formal value chains. The reported impacts ranged from 100 to over one million beneficiaries, in addition to environmental reductions of 30–50%, although methodological heterogeneity limited comparability. Financial constraints (67.3%), along with cultural resistance and institutional barriers, were identified as the main obstacles. Overall, the findings show that responsible organizational consumption can be an effective mechanism for reducing inequality if designed using systemic and integrated approaches, reinforcing the need for public policies, specialized financial instruments, and regulatory frameworks that enhance its transformative impact in favor of inclusive development in the country. Full article

Background/Objectives: New-onset atrial fibrillation (NOAF) frequently develops in patients with chronic coronary syndrome (CCS) and is associated with adverse cardiovascular outcomes. The C-reactive protein–to–albumin ratio (CAR) reflects systemic inflammation, whereas left atrial diameter (LAD) indicates structural cardiac remodeling. Their combined predictive role for NOAF in CCS remains uncertain. This study evaluated the predictive value of combined CAR and LAD for NOAF in CCS patients. Methods: We retrospectively analyzed 2431 CCS patients treated at the Third Affiliated Hospital of Sun Yat-sen University between 2012 and 2019. The primary endpoint was NOAF occurrence during follow-up. Receiver operating characteristic (ROC) analysis determined exploratory cutoff values for CAR (0.0429) and LAD (33.96 mm). Patients were categorized into four groups: Group 1 (low CAR–low LAD), Group 2 (high CAR–low LAD), Group 3 (low CAR–high LAD), and Group 4 (high CAR–high LAD). Cox proportional hazards, Kaplan-Meier, and subgroup analyses were conducted to evaluate associations with NOAF risk. Results: During a median follow-up of 4.96 years, 93 NOAF events were identified. Compared with the Group 1, patients with higher CAR and LAD showed significantly elevated NOAF risk (HR = 2.67, 95%CI 1.99–3.57, p < 0.001). The combined CAR–LAD model demonstrated superior predictive accuracy (AUC = 0.731, 95% CI = 0.654–0.765; p < 0.001) and consistent effects across most subgroups. Decision curve analysis confirmed greater net clinical benefit for the combined model. Conclusions: The integration of CAR and LAD serves as a simple, non-invasive, and effective tool for predicting NOAF in CCS patients. This dual-marker model facilitates early identification of high-risk individuals and support personalized preventive strategies in clinical practice. Full article

Background/Objectives: We aimed to construct a nomogram model for predicting arteriovenous fistula dysfunction risk and to conduct internal validation. Methods: The clinical data of 335 patients from the 8th Affiliated Hospital of Sun Yat-Sen University, collected from January 2019 to January 2024, were retrospectively analyzed. Among these patients, 103 were assigned to the arteriovenous fistula (AVF) dysfunction group, while 232 were in the non-dysfunction group. In this study, we first identified risk factors for AVF dysfunction using univariate and logistic regression analyses, and then constructed a prediction model by resampling the data. The model’s performance was evaluated using the C-index, ROC curve, calibration plot, and decision curve analysis, confirming its strong predictive ability and clinical value. Results: The results indicated that post-dialysis hypotension, abnormal fibrinogen levels, platelet abnormalities, total cholesterol levels, and diabetes mellitus emerged as independent risk factors for AVF dysfunction in MHD patients; however, total protein levels were a protective factor for AVF dysfunction. The model’s performance was assessed using the receiver operating characteristic (ROC) curve, the Hosmer–Lemeshow test, and the calibration curve. The ROC curve results demonstrated that the area under the curve (AUC) for the training set was 0.852 (0.799–0.904), while that for the validation set was 0.810 (0.715–0.906), indicating good calibration. The decision curve analysis revealed that the predictive nomogram was clinically useful when the threshold for intervention was set between a 15% and 78% probability of dysfunction. Conclusions: The nomogram prediction model constructed in this study can be used to predict the risk of autogenous arteriovenous fistula dysfunction in hemodialysis patients. Full article

Background/Objectives: The U.S. Centers for Disease Control and Prevention recommends some vaccinations using shared clinical decision-making (SCDM). SCDM recommendations are made when not every individual within a given age or risk group would benefit from vaccination, requiring collaborative discussions between patients and providers to assess risks and benefits. Pharmacists play a key role in implementing this recommendation and have frequent opportunities to engage with patients who may be eligible for SCDM-based vaccines. Because SCDM requires provider discussions to assess each patient’s eligibility for the vaccines under SCDM, trust may play a central role in the process. Trust has been suggested to affect patient’s participation in their care and their decision making preferences; however, the nature of this relationship in the context of SCDM vaccines and willingness to engage with pharmacists has yet to be investigated. As the CDC continues to expand the SCDM vaccine category, there is need to assess these. This study aimed to examine relationships between patient characteristics, trust in pharmacists, willingness to engage in SCDM, and vaccination decision control preference. Methods: Using quota sampling, cross-sectional data were collected from Alabama residents aged 18+ between February and March 2024 via a validated online questionnaire. Bivariate and multivariable logistic regression analyses were used to determine the association between trust, patient characteristics and willingness. Structural equation modeling was used to assess the direct and indirect relationships between trust, willingness and vaccination decision control preference. Statistical significance was set at p < 0.05. Results: A substantial portion (45.8%) of participants were unaware that certain vaccinations were based on SCDM. Multivariable logistic regression showed that race (Black vs. White, p = 0.001), age (25–34 vs. 18–24, p = 0.029), highest degree obtained (high school diploma or graduate equivalency degree vs. less than high school, p = 0.001; associate degree or vocational certificate vs. less than high school, p = 0.000; bachelor’s degree or higher vs. less than high school, p = 0.001), political affiliation (Democrat vs. Republican, p = 0.002), confidence in understanding health-related information (high vs. low, p =.029); moderate vs. low, p = 0.002), and patients’ trust in community pharmacists’ communication skills (p = 0.045) and benevolence (p = 0.001) towards their patients were significantly associated with patients’ willingness to engage in SCDM. Trust had a significant direct (p = 0.001) and indirect relationship (p = 0.000) with decision control preference through the willingness variable. Conclusions: Educational interventions are recommended to improve awareness and knowledge of SCDM vaccines among patients. Given their trusted role, pharmacists should actively build and maintain trust with patients, as this may help foster collaborative environments for discussion, encourage patient engagement in SCDM, and support more informed vaccination choices. Full article

Background: Out-of-hospital cardiac arrest (OHCA) survivors demonstrate wide variation in neurological outcomes due to hypoxic–ischemic brain injury. Early prognostic stratification in the emergency department is essential to inform clinical decisions. This study aimed to improve the revised Cardiac Arrest Syndrome for Therapeutic hypothermia (rCAST) score by incorporating additional clinical variables and to evaluate its ability to predict poor neurological outcomes. Methods: This multicenter observational study analyzed OHCA survivors treated with targeted temperature management (TTM) between October 2015 and December 2018 at 22 university-affiliated hospitals participating in the Korean Hypothermia Network prospective registry. The primary outcome was poor neurological status at one month, defined as a Cerebral Performance Category (CPC) score of 3–5. Independent predictors were identified using multivariable logistic regression and incorporated into a modified rCAST (mCAST) score. Results: Among 881 included patients, age > 65 years (odds ratio [OR], 13.87; 95% confidence interval [CI], 7.38–26.08) and absence of brainstem reflexes (OR, 2.31; 95% CI, 1.29–4.12) were identified as independent predictors and added to the mCAST score. The mCAST demonstrated higher prognostic accuracy than the original rCAST (area under the curve [AUC], 0.849 vs. 0.823; p < 0.001). In the high-severity group, the mCAST identified a higher poor outcome rate (95.1% vs. 90.9%) while reducing the proportion of patients in this group (20.7% vs. 31.3%). Conclusions: The mCAST score improves early prognostic accuracy during the immediate post-cardiac arrest period by incorporating age and brainstem reflexes and may offer refined risk stratification without compromising clinical feasibility. Full article

The implementation of the European (EU) Health Technology Assessment (HTA) Regulation 2021/2282 (EU HTAR) offers many opportunities, aimed at harmonizing HTA procedures and improving access to innovations; it also represents a significant challenge for the European healthcare system. Within the 2024 Health Policy Forum Italy meeting, different actors, stakeholders, and institutions had the opportunity to discuss major criticism and opportunities coming from the EU-HTA Regulation addressing future developments in the healthcare sector. Two groups, EU & Italy Pharmaceuticals and EU-Italy Medical Devices, worked distinctively on the EU HTAR by highlighting key issues that may pose challenges at both European and national levels, proposing potential solutions. The allocation of participants into two groups, according to their affiliation with either the pharmaceutical or the medical device sector, enhances the diversity of professional backgrounds and institutional perspectives, thereby fostering a more comprehensive and informed discussion. The recommendations highlighted by the two groups emphasize the need to promote cooperation among Member States, strengthen training for decision-makers, and develop a monitoring system to evaluate EU HTA’s impact. Full article

Background: Inborn errors of immunity (IEIs) are a heterogeneous group of rare disorders caused by genetic defects in one or more components of the immune system. The Jeffrey Modell Foundation’s (JMF) Ten Warning Signs are widely used for early detection; however, their diagnostic sensitivity is limited. Machine learning (ML) approaches may improve prediction accuracy by integrating additional clinical variables into decision-making frameworks. Methods: This retrospective study included 298 participants (98 IEI, 200 non-IEI) evaluated at a university-affiliated clinical immunology clinic between January and December 2020. IEI diagnoses were confirmed using European Society for Immunodeficiencies (ESID) criteria. Two datasets were constructed: one containing only JMF criteria and another combining JMF criteria with additional clinical variables. Four ML algorithms—random forest (RF), k-nearest neighbors (k-NN), support vector machine (SVM), and naive Bayes (NB)—were trained and optimized using nested 5-fold stratified cross-validation repeated three times. Performance metrics included accuracy, sensitivity, specificity, F1 score, Youden Index, and the area under the receiver operating characteristic curve (AUROC). SHapley Additive exPlanations (SHAP) were applied to evaluate feature importance. Results: Using only JMF criteria, the best-performing model was SVM (accuracy: 0.90 ± 0.04, sensitivity: 0.93 ± 0.05, AUROC: 0.91 ± 0.02). With the addition of clinical variables, the SVM achieved superior performance (accuracy: 0.94 ± 0.03, sensitivity: 0.97 ± 0.03, AUROC: 0.99 ± 0.00), outperforming both the classical JMF criteria (accuracy: 0.91, sensitivity: 0.87, AUROC: 0.90) and the JMF-only SVM model. SHAP analysis identified family history of early death, pneumonia history, and ICU admission as the most influential predictors. Conclusions: ML models, particularly SVM integrating JMF criteria with additional clinical variables, substantially improve IEI prediction compared with classical JMF criteria. Implementation of such models in clinical settings may facilitate earlier diagnosis and timely intervention, potentially reducing morbidity and healthcare burden in IEI patients. Full article

This study examines the responses of Reformed Christians living in the Carpathian Basin to ecclesiastical challenges that emerged after 1944. Focusing on the example of Reformed pastors in Transcarpathia, it explores the impact of the post-World War II transition on their communities—a shift not only in direction, but also in governance and national affiliation. The paper investigates a myth-forming episode within the collective memory of Reformed Christians, who found themselves in a unique borderland context. From a narrower perspective, the analysis reveals the relational and mental frameworks of pastoral groups, their differing interpretative coordinates, and the various ways they embodied their faith. These differences led to divergent understandings of the Church’s identity and mission, exposing internal mental fault lines. The fragmentation of group identity, brought to light during the 1947 conflict between the traditional national Church and the Eastern Friendship Circle, raised the possibility of schism. Intriguingly, atheist Soviet officials played a decisive role in preventing this split on two separate occasions. Full article

Chromosomal structural rearrangements (SR) can impair gametogenesis, increasing the risk of embryos carrying unbalanced chromosomal content (i.e., with a gain or loss of chromosomal material). In such cases, assisted reproduction technologies (ARTs) with preimplantation genetic testing for structural rearrangements (PGT-SR) is recommended to identify embryos with a normal or balanced karyotype. However, data on IVF laboratory outcomes in this context remain limited. This retrospective cohort study analyzed 548 ART cycles, comprising 129 with PGT-SR and 419 with PGT-A, conducted at a single university-affiliated center. Following propensity score matching, laboratory outcomes were compared using logistic regression. The fertilization rate was comparable between groups, but the PGT-SR group had significantly lower blastocyst development (36.7% vs. 47.1%) and top-quality blastocyst development rates (9.6% vs. 21.1%). No significant differences were found either in the blastocyst development rate on days 5, 6, 7, or in euploidy rates. In the PGT-SR cohort, the generalized linear mixed-effects model indicated no significant effect of carrier gender on the normal/balanced blastocyst rate, while the type of SR was strongly associated with it: non-reciprocal SRs yielded a higher rate of normal/balanced blastocysts (89.9%) compared to reciprocal translocations (45.7%). These findings indicate that patients undergoing PGT-SR generate fewer blastocysts available for biopsy, and that in cases involving reciprocal translocations, the proportion of normal/balanced blastocysts suitable for transfer is significantly reduced. These results underscore the importance of personalized counseling in managing expectations and supporting informed clinical decision-making. Full article

The recent debate on Environmental, Social, and Governance (ESG) factors has focused primarily on financial decision making and risk management from the perspectives of developed economies. However, in most developing countries, ESG risk models for mortgage lenders are very limited. In most of these countries, ESG-rating providers employ widely varying methodologies and disclosure policies, often resulting in divergent assessments of the same organization. This research develops a pilot statistical-analysis, dual-horizon ESG risk model specific to the Mexican mortgage industry, which provides a better understanding of how ESG risk could evolve over time across financial, operational, regulatory, and reputational dimensions in Mexico. This dual-horizon ESG framework considers a two-year short-term risk assessment and a ten-year long-term risk assessment. This research integrates expert opinions with a scoring system that improves on traditional methods. Dependability and internal consistency are tested using the Intraclass Correlation Coefficient (ICC) and Cronbach’s alpha. Radar chart visualization and cluster analysis are used to visualize the empirical results. The empirical findings show that environmental risk has strong temporal effects, and the perceived severity is 20% higher over the longer time horizon. Furthermore, social risk exhibits high variability, identifying it as a critical risk for financial stability and regulatory compliance. Cluster analysis identifies systematic patterns in expert opinions that determine two groups, making the qualitative findings derived from radar plots more robust. Group 0 (75% of experts) has an institutional view about ESG risks. Group 1 (25% of experts) aligns with an affiliation to large financial institutions. Finally, this research identifies three key sustainability challenges for the mortgage sector in Mexico: exposure to climate-induced stress, fragmented regulatory frameworks, and social inequality. Full article

This study investigates the relationship between the CEO characteristics and investment–cash flow sensitivity (ICFS) of Indian manufacturing firms. By using the GMM technique, this study finds that CEO characteristics reduce ICFS. Further, this study examines the moderating role of business group-affiliated firms, independent firms (non-business group-affiliated firms), and firm size on the relationship between CEO characteristics and ICFS. The results reveal that group affiliation moderates the effectiveness of CEO characteristics in reducing ICFS. In addition to this, independent firms rely more heavily on the individual capabilities of CEOs to overcome financial constraints and mitigate ICFS, whereas group firms benefit from structural advantages that diminish the relative impact of CEO characteristics on ICFS. Additionally, this study finds that firm size also moderates the relationship between CEO characteristics and ICFS. The results reveal that CEO characteristics significantly reduce ICFS, with a more pronounced effect in small-sized independent firms compared to their larger counterparts. However, in group-affiliated firms, CEO characteristics have a minimal effect on ICFS, and this impact remains consistent across small and large group firms. These findings offer valuable insights for firms, lending institutions, and investors, emphasizing the role of CEO characteristics in shaping financial decision making, especially in independent and smaller firms. Full article

Several studies have investigated the determinants of firms’ capital structure choices. Though an intercorporate loan is an essential source of corporate debt, there are no studies that examine the determinants of firms’ preference to use the intercorporate loan as a source of debt. This study examines the relevance of the conventional capital structure determinants in explaining firms’ tendency to use intercorporate loans. The study is based on a dataset of 53,112 firm-year observations comprising 3739 non-financial listed Indian firms for 21 years from 2002 to 2022. The random effect logistic regression model is used to investigate the objectives. The conventional capital structure determinants are relevant in explaining firms’ decisions to use intercorporate loans. Firm size, asset tangibility, and earnings volatility favorably influence the tendency to use intercorporate loans, whereas profitability, growth, uniqueness, dividend payment, ownership concentration, and foreign promoter holdings adversely affect the same. The results reveal that the influence of firm size, uniqueness, earnings volatility, and ownership concentration are not unidirectional for group-affiliated and standalone firms. The findings are mostly consistent with the arguments of conventional capital structure theories. The results of this study will be pragmatic for financial managers in their capital structure decisions. Full article