Search Results (46)

Background/Objectives: Large language models (LLMs) and generative AI are rapidly being integrated into healthy ageing initiatives for tasks ranging from companionship and cognitive support to personalised health advice and reduction in social isolation among older adults. Current ethical discussions predominantly address bias, privacy, and accuracy, leaving unresolved three critical governance questions: How do LLM sentiments towards transformative technologies diverge from human values in ageing contexts? What epistemic status do LLM outputs hold when applied to geriatric care? When is trust in those outputs justified for older adults? And who bears responsibility when AI-informed decisions affect functional ability or well-being? Methods: The framework was developed through normative conceptual analysis, synthesizing philosophical principles of medical knowledge and trust, ethical theories of responsibility, empirical evidence on LLM sentiment divergence, digital ageism, and applications of AI in geriatric care (structured searches in PubMed, PhilPapers, and relevant databases, January 2020–March 2026). Results: The integrated framework produces (i) adaptation of SAIA for multidimensional evaluation of human–machine value convergence specific to healthy ageing values (functional ability, autonomy, dignity, equity); (ii) a four-tier classification of LLM outputs tailored to geriatric scenarios; (iii) conditions for warranted trust calibrated to age-related vulnerabilities such as cognitive decline and digital divide; and (iv) responsibility allocation via RACI models with testable hypotheses linking governance design to trust calibration and patient safety outcomes. Conclusions: Without explicit societal alignment and epistemic governance, generative AI risks reinforcing benevolent ageism, automation bias, and responsibility gaps in healthy ageing. The 2025–2027 period offers a decisive window to shape institutional norms that place functional capacity, human dignity, and value convergence at the centre of AI deployment in geriatric care. Full article

Background: Sepsis remains a leading cause of mortality in emergency and intensive care settings, with early diagnosis representing a critical determinant of patient outcomes. Despite advances in biomarker discovery, integrating platelet-derived metabolic signatures with explainable machine learning frameworks for sepsis prediction remains underexplored. The clinical adoption of predictive models has been hindered by the “black box” nature of conventional algorithms, limiting clinician trust and understanding. Objective: This study aimed to evaluate and validate an interpretable machine learning model utilizing platelet metabolomics data for accurate sepsis prediction while providing clinically meaningful explanations of the underlying metabolic disturbances that could inform therapeutic decision-making. Methods: We analyzed metabolomics data, comprising 25 sepsis patients diagnosed according to Sepsis-3 criteria and 14 age- and gender-matched non-sepsis from the emergency department. Platelet metabolite profiles were obtained via quantitative ¹H-NMR spectroscopy. Five machine learning algorithms were evaluated: Explainable Boosting Machine (EBM), Support Vector Machine (SVM), Logistic Regression (LR), Gradient Boosting Machine (GBM), and AdaBoost. Three biologically motivated metabolite ratios (adenosine triphosphate/adenosine diphosphate (ATP/ADP), ATP/adenosine monophosphate (AMP), Glutamine/Glutamate) were derived as additional features, yielding 22 candidate variables. Models were evaluated using a fully nested leave-one-out cross-validation (LOOCV) framework in which log transformation, KNN imputation, BorderlineSMOTE class balancing, and hyperparameter optimisation were performed exclusively within each training fold. Global and local interpretability analyses were performed to identify discriminative metabolites. Results: EBM achieved the highest ROC-AUC (0.864; 95% CI: 0.736–1.000), the highest PR-AUC (0.902; 95% CI: 0.783–0.997), and the best Brier score (0.189; 95% CI: 0.130–0.258) among all evaluated models, with sensitivity 0.880 (95% CI: 0.640–1.000; TP = 22/25) and specificity 0.714 (95% CI: 0.357–1.000; TN = 10/14). Global feature importance identified Carnitine, myo-Inositol, ADP, and O-Phosphoethanolamine as the leading single-feature predictors, alongside three pairwise interaction terms reflecting non-additive energy–amino acid metabolic relationships. Local explanations demonstrated that the ADP–Creatine interaction, Glutamine, and myo-Inositol drove correct sepsis classification in a representative true positive case. Conclusions: The EBM model demonstrated the highest discriminative performance and best calibration among all evaluated models, providing transparent mechanistic insights through global feature importance, and patient-level local explanations. These findings position the proposed framework as a proof-of-concept warranting external validation in larger, multi-centre cohorts before any clinical application is considered. Full article

Much research on misinformation has focused on claims that challenge mainstream views. This study instead examines misinformation that is aligned with dominant narratives in mainstream news media. It uses an epistemically grounded definition of misinformation and a measurement approach that accounts for the complexity of politically relevant misinformation. The analysis centers on 20 false or misleading claims about major international issues, evaluated by a sample of 1590 survey respondents in German-speaking Switzerland. Across these claims, the population-weighted share of “fully agree” responses ranged from 6% to 50%. The probability of acceptance of misinformation was significantly associated (p < 0.05) with several factors: male gender (+0.04); medium or higher education (−0.06 and −0.05, respectively); more than 30 min of daily news consumption, compared with less (+0.06); identification with Green (+0.09) and Liberal party (+0.07, relative to Center party); medium trust in government (−0.04), relative to high trust; and a non-mainstream primary news source (−0.28), relative to public television. A similar pattern emerges for the subset of the claims that had been publicly corrected. Notable differences from patterns reported for non-mainstream misinformation highlight the importance of the type of misinformation under study. Taken together—and in light of evidence that news consumers expect truthful and impartial information—these findings suggest that misinformation in mainstream news media warrants greater attention in both research and regulatory policy. Full article

Background/Objectives: Large language models (LLMs) such as ChatGPT are rapidly being integrated into healthcare for tasks ranging from clinical documentation to diagnostic support. Current ethical discussions focus predominantly on bias, privacy, and accuracy, leaving three critical governance questions unresolved: What kind of knowledge does an LLM output represent in clinical reasoning? When is a clinician’s or patient’s trust in that output justified? Who bears responsibility when an AI-informed decision leads to patient harm? This study proposes the Epistemic Authority–Trust–Responsibility (ETR) Architecture, a normative conceptual framework that addresses these three questions as an integrated governance challenge. Methods: The framework was developed through normative conceptual analysis—a method that constructs governance proposals by synthesising philosophical principles, ethical theories, and empirical evidence. The literature was identified through structured searches of PubMed, PhilPapers, and EUR-Lex (January 2020–March 2026), drawing on the philosophy of medical knowledge, the ethics of trust and testimony, and the moral philosophy of responsibility. Results: The ETR Architecture produces four outputs: (i) a four-tier classification system that distinguishes LLM outputs—from administrative drafts to clinical evidence claims—and matches each tier to appropriate verification requirements; (ii) the concept of the ‘epistemic placebo’, formally defined as a governance measure that creates a documented appearance of compliance while lacking at least one operative element of genuine oversight; (iii) a model specifying four conditions under which trust in healthcare AI is justified; (iv) four testable hypotheses with associated research designs connecting governance design to trust calibration and patient safety. Conclusions: The 2025–2027 regulatory transition period offers a critical window for shaping how healthcare institutions govern AI. We argue that deploying LLMs without explicitly classifying their outputs and building appropriate oversight risks allows governance norms to be set by technology vendors rather than by evidence-informed, patient-centred policy. Full article

The evolving digital landscape, including artificial intelligence (AI) and its generative forms, is changing how younger generations learn. As students utilize generative AI systems, they cultivate trust in such technology to support their current and long-term learning. The objective of this study was to investigate the relationship between generative AI use among students in health professions and their trust in this technology to support their lifelong learning as future health professionals. This study employed a survey methodology using a cross-sectional study design. The survey included sociodemographic variables and questions regarding students’ generative AI use and their trust in this technology to support their lifelong learning. Descriptive and inferential statistical procedures were used to analyze the data. A total of 558 students representing various health professions responded to the survey. In the regression analysis, after controlling for student’s sex and location variables, greater generative AI use was associated with students’ increased trust in this technology to support their lifelong learning (beta = 0.58, p < 0.001), explaining close to 40% of the total variance. Given the rapidly evolving digital landscape, this finding warrants further study, with implications for training of the future health workforce. Full article

Learning objectives are a foundational component of undergraduate medical education and are widely assumed to guide students’ studying, planning, and self-assessment. Despite these assumptions, limited empirical work has examined how medical students actually use them in practice. This study examined learning objectives as strategic learning tools by contrasting educator-endorsed strategies identified through a scoping review with student-described practices derived from qualitative focus groups with pre-clerkship medical students. The scoping review identified educator-centered strategies emphasizing proactive planning, self-directed learning, and assessment alignment. Focus group findings revealed that students engaged with objectives selectively and reactively. Most often after learning events for self-testing, gap identification, and content triage; a pattern markedly different from educator assumptions of routine, front-end use. Two novel findings emerged: students’ use was shaped by conditional trust in objective quality and assessment alignment, and a subset of students independently developed technology-enabled adaptations (including AI-generated practice questions and flashcard conversion) not reflected in the existing educator literature. These findings highlight a meaningful gap between prescriptive guidance and student learning realities, and suggest that more learner-aligned approaches to supporting learning objective use are warranted. Full article

Public debates about psychoactive substances have traditionally been organized around the pharmacology of compounds and the institutional control of supply. In digitally mediated societies, however, the pathways through which people encounter psychoactives are increasingly informational: search engines, recommender systems, social platforms, and—distinctively—conversational AI. These systems do not merely deliver neutral facts. They rank, frame, personalize, and conversationally validate claims in ways that can shape perceived norms, acceptable risk thresholds, and willingness to seek help. This opinion advances the concept of AI-mediated exposure to capture how algorithmic curation and interactive dialogue become upstream determinants of psychoactive-related harms and benefits across the continuum from everyday medicines to non-medical use. From a social-scientific ethics perspective, the central question is not whether AI is “good” or “bad,” but what obligations apply when AI performs interpretive authority in contexts characterized by vulnerability, stigma, and unequal access to trusted expertise. The paper argues for an ethics-centered governance framework grounded in four commitments: epistemic responsibility (how claims are generated, warranted, and communicated), relational responsibility (how users are treated in moments of uncertainty, distress, and stigma), distributive justice (who benefits and who bears risk under unequal conditions), and accountability (how behavior is evaluated, contested, and corrected over time). The aim is to treat conversational AI as a public-facing institution whose design choices must be ethically legible and publicly contestable, oriented toward harm reduction without intensifying surveillance, moralization, or inequity. Full article

This study examined the relationship between trust in COVID-19 information sources and self-rated health (SRH) among Latino Day Laborers (LDLs) and whether mental health mediated this association. Participants (N = 300) recruited at 18 job-seeking locations were interviewed in Spanish during November and December 2021. Validated scales were used to measure trust in formal (e.g., broadcast news, newspapers, and radio) and informal sources (e.g., friends, family, and social media) and mental health (depression, anxiety, and stress), with SRH measured with a single item. Mediation analysis was conducted using Hayes’ SPSS PROCESS macro. Higher trust in formal sources of information was related to lower SRH, but this relationship was not mediated by mental health. However, depression and anxiety were associated with a decrease in SRH. There were no significant direct or indirect effects between trust in informal sources and SRH. Depression and anxiety remained significant predictors of lower SRH. Further research is warranted on the mechanisms underlying these associations and the differential impact of information sources on vulnerable populations, such as LDLs, during health crises. Full article

ESG (Environmental, Social, and Governance) considerations are increasingly influencing REIT (real estate investment trust) investment decisions; however, empirical evidence on the ESG–financial performance nexus in infrastructure REITs remains scarce. Given China’s nascent highway REIT market, this exploratory study proposes a hybrid modeling framework that integrates static econometric analysis with dynamic system simulation to examine how ESG factors affect investment risk. Using VaR (Value at Risk) analysis and an ESG-adjusted CAPM (Capital Asset Pricing Model) on 10 Chinese highway REITs (2021Q2–2025Q2), we constructed a composite ESG indicator via a weighted proxy approach. We identified three key findings testing hypotheses linked to ESG finance theory; these findings support H1 (non-monotonic VaR reduction) and partially confirm H2 (inverted-U path with lag): (1) the ESG-adjusted weighted average cost of capital (WACC) exhibits an inverted U-shaped trajectory with post-peak oscillations and an overall 20-month implementation lag (derived from system dynamics simulations) to efficiency realization; (2) the results suggest initial evidence showing that an ESG investment intensity (IEP ≈ 0.40, representing moderate ESG resource allocation) may indicate potential outperformance over both under-investment (−5.0% deviation in risk-adjusted returns) and over-investment (−8.0% deviation in risk-adjusted returns), though with uncertainty in static estimates; and (3) system dynamics validation suggests potential predictive accuracy. These preliminary findings challenge linear ESG–performance assumptions and offer dynamic risk assessment tools; nevertheless, as an exploratory study, they warrant replication in larger and more diverse samples. Thus, the results should be regarded as preliminary guidance rather than conclusive evidence, with further validation needed to confirm generalizability. Full article

The rise of the smart city reflects a transformative shift in urban development, defined by the integration of advanced technologies and data-driven solutions seeking to address rapid urbanization, environmental externalities, and the ever-increasing pressing need for optimal resource use. Nevertheless, a better understanding of the factors that shape citizens’ behavioral intentions towards smart-city living is becoming a sheer necessity. This study is among the first to empirically examine determinants describing the propensity to use smart-city services in an urban setting of south-eastern Europe. In this regard, we employ the smart-city stakeholders’ adoption (SSA) model in order to shed light on smart-city technology acceptance, further focusing on the underlying impact of demographics in shaping citizen attitudes and perceptions. Findings suggest that key predictors of acceptance (latent variables describing self-efficacy, price value, and trust in technology), all positively affect behavioral intention while the non-significance of effort expectancy contradicts the relevant results of previous studies and warrants further investigation. Furthermore, the analysis supports the theorized indirect effects of the model, whereas perceived privacy and perceived security both influence behavioral intention via trust in technology, while price value mediates the effect of citizen’s trust in government. The role of demographics was examined for potential moderating effects and was found to be significant, particularly in the case of age and education. Even though the demographic moderators we opted for do not substantially affect the explanatory power of the model, they seem to improve its specificity, particularly regarding perceptions on effort expectancy across the different demographic groups. Such results offer actionable insights on the relevance of smart-city acceptance models to the different demographic groups and in tailoring policies according to demographic segmentation groups with common characteristics. Full article

Background/Objectives: The increasing interest in functional foods has highlighted the need to better understand consumer perceptions and their influence on dietary behaviours. This study aimed to evaluate the psychometric properties of the Questionnaire for the Assessment of Perception of Functional Foods (QAPAF) and apply it to a Portuguese adult population to explore associations with sociodemographic and lifestyle factors. Methods: Participants were recruited through convenience sampling; the achieved sample was predominantly female and highly educated. The 17-item QAPAF was assessed through exploratory factor analysis (EFA), internal consistency (Cronbach’s alpha), and test–retest reliability. Associations between QAPAF scores and participant characteristics were analysed using non-parametric tests. Results: EFA supported a four-factor structure, explaining 58.8% of total variance. Internal consistency was acceptable (α = 0.70), and test–retest analysis (n = 25) showed no significant score differences, indicating temporal stability. QAPAF scores were significantly higher among participants with higher education and among non-smokers and non-drinkers. No associations were found with sex, BMI, or income. Participants with correct understanding of functional foods were more likely to reject misconceptions and express trust in professional recommendations. Conclusions: The QAPAF is a valid and reliable tool for assessing functional food perceptions. Its application provides insights into consumer attitudes and may support the design of targeted food literacy interventions. Generalizability is limited by the convenience sampling and by the predominance of female and highly educated participants; external validation in more diverse samples and cultural contexts is warranted. Full article

As Generation Z gradually becomes the dominant consumer group, the catering industry, as a critical sector affecting people’s livelihood, warrants an in-depth investigation into its consumption decision mechanisms. This study, grounded in the online–merge–offline (OMO) context, empirically examines the impact mechanism of frontline employee service competence on the repurchase decisions of Generation Z consumers in the foodservice sector, while testing the mediating roles of customer satisfaction and brand trust, as well as the moderating effect of the OMO scenario. Data were collected via a survey of 326 Generation Z customers who consumed in integrated OMO dining environments. Partial least squares structural equation modeling (PLS-SEM) was employed for the data analysis. The findings reveal that frontline employee service competence significantly and positively influences consumer repurchase intention and customer satisfaction, but does not have a significant effect on brand trust. Customer satisfaction fully mediates the relationship between employee service competence and repurchase decisions, whereas brand trust, despite having the strongest direct effect on repurchase intention, is predominantly shaped by systemic factors such as food safety and supply chain transparency, rendering its mediating pathway non-significant. Furthermore, the OMO context does not exhibit a significant moderating effect between employee service competence and customer satisfaction, nor between employee service competence and brand trust, reflecting that the current digital integration has yet to effectively address Generation Z’s core needs for privacy protection and emotional resonance. This study elucidates the “contact–cognition–behavior” pathway by which service competence influences consumer decision-making through satisfaction, while clarifying the systemic formation mechanism of brand trust. Based on these results, it is recommended that enterprises prioritize emotional service training for frontline employees to enhance satisfaction, build brand trust through ingredient traceability systems, and optimize OMO scenario design to better align with Generation Z’s expectations for emotional interaction. Full article

Background/Objectives: Vaccination rates in South Tyrol, Northern Italy, remain among the lowest in the country. This study investigated whether health literacy is associated with vaccine uptake in this multilingual region. Methods: A representative cross-sectional survey (n = 2090) was conducted using the validated European Health Literacy Survey Questionnaire (HLS-EU-Q16) to assess health literacy. Vaccine uptake was evaluated on a 4-point Likert scale and analysed by age group (18–54 years; ≥55 years). Associations were explored using ANCOVA and multivariate logistic regression models. Results: Among younger adults (18–54 years), higher health literacy was significantly associated with greater vaccine uptake, particularly when compared with individuals with missing health literacy data. Health literacy was not a significant predictor for vaccine uptake in the older age group (≥55 years). Instead, vaccine uptake correlated with trust in healthcare providers, the presence of chronic diseases, and educational level. Differences in health literacy were notable across language groups, but these did not interact with vaccination behaviour. Conclusions: Building trust and targeting individuals with lower educational attainment are key strategies for improving vaccination rates across language groups. Although health literacy plays a secondary role, efforts to enhance it, especially among the German-speaking population, are still warranted. Younger individuals with missing health literacy scores, often with a migration background and low trust in healthcare, should be prioritised in vaccination and health literacy campaigns. Full article

Support of parents with intellectual disabilities should be long-term and start as early as possible. They, however, often come only into view late. The aim of the present study was to examine the barriers and facilitators in reaching and supporting parents with intellectual disabilities and young children in the Netherlands. Fourteen professionals, three volunteers, and three mothers with intellectual disabilities participated in four focus groups. Results revealed four themes: (1) improve professional development, (2) mutual professional/parent distrust, (3) strengthen support network systems, and (4) improve child welfare system responses. Professionals and policymakers lack knowledge of parents with intellectual disabilities and skills to build trust and support them adequately. Professional development is warranted. The informal network and involvement of fathers is limited and, thus, could be increased. Finally, the way the system is organized impedes adequate support. Long-term involvement and, thus, a different use of resources is needed. Reaching parents with intellectual disabilities as early as possible is important but challenging due to multiple interacting factors. Integral actions at multiple levels are essential to improve preventive care for these parents. Full article

Background/Objectives: Pediatric hypertension is an important and impactful condition. The 2017 American Academy of Pediatrics clinical practice guidelines provide recommendations for identifying and managing this condition within primary care. However, the perspectives and self-described practices of physicians are largely absent in the current evidence base. We aim to fill this gap through our qualitative investigation of physicians’ perceptions and practices related to the screening, follow-up, and management of primary pediatric hypertension. Methods: We conducted semi-structured interviews with pediatric and family medicine physicians from the largest healthcare system in central Massachusetts. The interviews explored physicians’ perceptions, and practices related specifically to pediatric blood pressure screening, follow-up for high blood pressures, and management of primary hypertension. We used rapid qualitative analysis to synthesize data into the resulting themes. Results: Eleven interviews were conducted. Resulting themes included: (1) physicians are generally concerned about pediatric hypertension and familiar with guidelines, but other concerns often take precedence, (2) blood pressure screening occurs mainly during yearly well visits, (3) physicians do not trust high blood pressure readings, (4) follow-up after high blood pressure readings varies, and (5) primary care physicians typically refer to specialists for hypertension management. Conclusions: This study expands current literature by providing salient context to the state of pediatric blood pressure screening, follow-up, and primary hypertension management after the 2017 guidelines among primary care physicians affiliated with an academic medical center. Our findings related to physicians’ trust in electronic health record flags and the utility of follow-up by school nurses warrant further investigation. Full article