Search Results (1,717)

With the popularization of social media, information related to public health events has seen explosive growth online, making it essential to accurately identify informative tweets with decision-making and management value for public health emergency response and risk monitoring. However, existing methods often suffer performance degradation during cross-event transfer due to differences in data distribution, and research specifically targeting public health events remains limited. To address this, we propose the Label Confusion Domain Adversarial Network (LCDAN), which innovatively integrates label confusion with domain adaptation to enhance the detection of informative tweets across different public health events. First, LCDAN employs an adversarial domain adaptation model to learn cross-domain feature representation. Second, it dynamically evaluates the importance of different source domain samples to the target domain through label confusion to optimize the migration effect. Experiments were conducted on datasets related to COVID-19, Ebola disease, and Middle East Respiratory Syndrome public health events. The results demonstrate that LCDAN significantly outperforms existing methods across all tasks. This research provides an effective tool for information detection during public health emergencies, with substantial theoretical and practical implications. Full article

Background: Vaccine hesitancy (VH) represents a growing concern among healthcare professionals and students, potentially undermining public health efforts. Nursing, pediatric nursing, and midwifery students are future vaccinators and educators, making it essential to understand their attitudes, knowledge, and confidence toward vaccination. This study aims to assess vaccine-related perceptions and behaviors among these student populations in an Italian university. Methods: A cross-sectional survey was conducted between November 2022 and February 2024 at the University of Rome “Tor Vergata”. A structured, anonymous questionnaire, including the Vaccination Attitudes Examination (VAX) scale, vaccine knowledge items, and sources of information, was administered to students in nursing (n = 205), pediatric nursing (n = 46), and midwifery (n = 21). Statistical analyses included descriptive statistics, ANOVA, post hoc tests, and Mann–Whitney U tests. Results: Among the 272 participants, 20.6% reported refusing at least one recommended vaccine, and 18.4% delayed vaccination for non-medical reasons. Vaccine knowledge and confidence increased significantly with academic progression (p < 0.001). Midwifery students showed both the highest concern for long-term vaccine effects and the greatest confidence in vaccine safety. Institutional and scientific sources were the most trusted, though traditional and non-institutional media also influenced perceptions, particularly among midwifery students. Conclusions: Despite high COVID-19 vaccine uptake, VH persists among health professional students. Discipline-specific patterns highlight the need for early, targeted educational strategies to enhance vaccine literacy and reduce hesitancy. Tailored training may empower future professionals to become informed and credible advocates for vaccination. Full article

Artificial intelligence (AI) techniques—ranging from hybrid mechanistic–machine learning (ML) ensembles to gradient-boosted decision trees, support-vector machines, and deep neural networks—are transforming the management of seasonal influenza, respiratory syncytial virus (RSV), human immunodeficiency virus (HIV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Symptom-based triage models using eXtreme Gradient Boosting (XGBoost) and Random Forests, as well as imaging classifiers built on convolutional neural networks (CNNs), have improved diagnostic accuracy across respiratory infections. Transformer-based architectures and social media surveillance pipelines have enabled real-time monitoring of COVID-19. In HIV research, support-vector machines (SVMs), logistic regression, and deep neural network (DNN) frameworks advance viral-protein classification and drug-resistance mapping, accelerating antiviral and vaccine discovery. Despite these successes, persistent challenges remain—data heterogeneity, limited model interpretability, hallucinations in large language models (LLMs), and infrastructure gaps in low-resource settings. We recommend standardized open-access data pipelines and integration of explainable-AI methodologies to ensure safe, equitable deployment of AI-driven interventions in future viral-outbreak responses. Full article

The uptake of the COVID-19 and seasonal influenza (SI) vaccines have decreased in Europe and especially in Malta. The present study aimed to investigate the attitudes toward COVID-19 and SI vaccines and determine if individuals perceive that these vaccines are relevant to protect their health and identify reasons for their responses. A cross-sectional study using an anonymous questionnaire, informed by the Theory of Planned Behavior, addressing behavior beliefs and attitudes, and targeted at adult residents in Malta, was designed on Google Forms and disseminated using social media between January and March 2024. A total of 555 responses were received. The majority of respondents did not take/intend to take the COVID-19 (75%, n = 417) or SI (64.3%, n = 362) vaccines, with females being less likely to do so (p = 0.033). Perceived lack of safety (31.3%, n = 174) was the primary reason for rejecting the COVID-19 vaccine, and perceived lack of a threat from SI (26%, n = 144) was the reason for rejecting the SI vaccine. Those having chronic conditions were positively associated with uptake of both vaccines. In the post-pandemic era, these vaccines are not envisaged as having a major role in protecting one’s health. A high degree of skepticism especially toward the combined COVID-19 and SI vaccine in terms of safety, mostly in women, is still present. Full article

The influence of social media personalities has grown significantly, especially among youth audiences who spend substantial time on platforms like TikTok. The emergence and popularity of different types of social media influencers accelerated during the COVID-19 pandemic in many countries, including South Africa. In turn, this period also saw a surge in youth audiences following these influencers. This rapid growth of influencer followings among young people is largely driven by specific types of content that resonate with them, thus encouraging continued engagement. However, the benefits that these young followers gain from engaging with various influencers and the factors driving their preferences for specific influencers remain underexplored, particularly within the context of South African students within higher education. Therefore, this study explores the types of social media influencers most followed by university students at a South African University and investigates the key factors that drive their preferences. A structured online questionnaire was distributed, gathering both multiple-choice and open-ended responses from students. The data were analyzed using categorical frequency counts and thematic analysis. The data highlight how students actively turn to influencers as emotional anchors, role models, and sources of practical guidance. Their engagement reflects a deep need for connection, inspiration, and identity formation in a challenging academic and social environment. These patterns show that influencer content is not just entertainment but plays a critical developmental role. Understanding these motivations helps educators, policymakers, and brands to align better with youth values. The significance of these results lies in how influencer content is now coming in to fill the emotional, cultural, and educational gaps left by traditional systems among the young South African university students in this modern era. Full article

This study aimed to explore how social media usage influenced both parent and adolescent mental health and social identity during and after the COVID-19 pandemic through the theoretical foundational lens of social comparison theory. In-depth interviews with 24 mothers of adolescent children (ages 10–19) were conducted to address the research questions. Qualitative thematic analysis of the interview transcripts revealed eight emerging themes: (1) learning and entertainment, (2) maternal fears related to content binging and cyberbullying, (3) finding connection and comfort through social media during the pandemic, (4) ongoing digital care work as lasting maternal labor, (5) iterative dialogue: platform restrictions and content curation boundaries, (6) upward and downward social comparison, (7) fear of missing out (FoMO), and (8) third-person perception (TPP). The findings show that mothers perceive social media usage as either beneficial or harmful among adolescents (their children); upward and downward social comparison via social media exhibits more dynamic mechanisms. Moreover, this study enhances our theoretical understanding by linking social media usage to social identity, social comparison, and mental health during a global health crisis. Full article

Background and Aim: Digital surveillance, which utilizes data from social media, search engines, and other online platforms, has emerged as an innovative approach for the early detection of infectious disease outbreaks. This scoping review aimed to systematically map and characterize the methodologies, performance metrics, and limitations of digital surveillance tools compared to traditional epidemiological monitoring. Methods: A scoping review was conducted in accordance with the Joanna Briggs Institute and PRISMA-SCR guidelines. Scientific databases including PubMed, Scopus, and Web of Science were searched, incorporating both empirical studies and systematic reviews without language restrictions. Key elements analyzed included digital sources, analytical algorithms, accuracy metrics, and validation against official surveillance data. Results: The reviewed studies demonstrate that digital surveillance can provide significant lead times (from days to several weeks) compared to traditional systems. While performance varies by platform and disease, many models showed strong correlations (r > 0.8) with official case data and achieved low predictive errors, particularly for influenza and COVID-19. Google Trends and X (formerly Twitter) emerged as the most frequently used sources, often analyzed using supervised regression, Bayesian models, and ARIMA techniques. Conclusions: While digital surveillance shows strong predictive capabilities, it faces challenges related to data quality and representativeness. Key recommendations include the development of standardized reporting guidelines to improve comparability across studies, the use of statistical techniques like stratification and model weighting to mitigate demographic biases, and leveraging advanced artificial intelligence to differentiate genuine health signals from media-driven noise. These steps are crucial for enhancing the reliability and equity of digital epidemiological monitoring. Full article

Infectious disease outbreaks amplify the influence of stressors on psychological conditions. The purpose of this study was to analyze the disturbing influence of COVID-19 outbreak-related information and the influence of trust on the Serbian healthcare system and COVID-19 preventive measures on anxiety and depression. An anonymous online questionnaire assessing the demographic information, disturbance level and causes, and levels of anxiety and depression has been distributed to the participants, divided into student and non-student groups. The non-student group was further divided into healthcare, military, and education workers. Anxiety and depression levels, as well as the level of decreased trust in COVID-19-related preventive measures, were higher among students compared to non-students (p = 0.011). Higher anxiety and depression levels, and higher influence of the COVID-19 outbreak on those levels, were observed in education and healthcare workers, compared to military personnel. Medical doctors reported a higher level of trust in the healthcare system compared to nurses (p = 0.023). Trust in the healthcare system increased more frequently compared to the pre-vaccination period among medical doctors, compared to nurses (p = 0.040). Higher anxiety and depression and lower public trust levels in students and workers in education and the healthcare sector indicate a need to focus on these important society members during public health emergencies. Full article

Background/Objectives: Digital technologies have become increasingly integrated into the daily lives of children and adolescents, largely because their interactive and visually engaging design is particularly suited to the younger users. The COVID-19 pandemic further accelerated this trend, significantly lowering the average age of access to the digital devices. However, scientific consensus remains divided regarding the developmental impact of digital media use—particularly its cognitive, motor, and emotional consequences—depending on whether the use is passive or active. This review aims to explore these effects across developmental stages, focusing on both behavioral and neurobiological dimensions, and to identify emerging risks and protective factors associated with digital engagement. Methods: A PRISMA review was conducted on the impact of digital media use among pre-school children and adolescents. Behavioral, psychosocial, and neurobiological aspects were examined, with specific attention to epigenetic changes, techno-stress, digital overstimulation, and immersive technologies (e.g., virtual and augmented reality). Results: The findings suggest that passive digital consumption is more often associated with negative outcomes, such as impaired attention and emotional regulation, especially in younger children. Active and guided use may offer cognitive benefits. Neurobiological research indicates that chronic exposure to digital stimuli may affect stress regulation and neural development, possibly via epigenetic mechanisms. Effects vary across developmental stages and individual vulnerabilities. Conclusions: A nuanced understanding of digital engagement is essential. While certain technologies can support development, excessive or unguided use may pose risks. This review provides age-specific recommendations to foster balanced and healthy technology use in children and adolescents. Full article

This study provides a detailed analysis of COVID-19-related social media discourse in Italy, using 535,886 tweets from 10 major cities between 30 August 2020 and 8 June 2021. The tweets were translated from Italian to English for analysis. A multifaceted methodology was employed: Latent Dirichlet Allocation (LDA) identified 20 key themes; sentiment analysis, using TextBlob, Flair, and TweetNLP, and emotion recognition using TweetNLP, revealed the emotional tone of the discourse, with 453 tweets unanimously positive across all algorithms. TextBlob was used for lexical analysis to rank the most salient positive and negative terms. The results indicated that positive sentiments centered on hope, safety measures, and vaccination progress, while negative sentiments focused on fear, death, and quarantine frustrations. This research offers valuable insights for public health officials, enabling tailored messaging, real-time strategy monitoring, and agile policymaking during the pandemic, with implications for future health crises. Full article

The COVID-19 pandemic and recent viral outbreaks have highlighted the need for viral diagnostics that balance accuracy with accessibility. While traditional laboratory methods remain essential, point-of-care solutions are critical for decentralized testing at the population level. However, a gap persists between academic proof-of-concept studies and clinically viable tools, with novel technologies remaining inaccessible to clinics due to cost, complexity, training, and logistical constraints. Recent advances in surface functionalization, assay simplification, multiplexing, and performance in complex media have improved the feasibility of both optical and non-optical sensing techniques. These innovations, coupled with scalable manufacturing methods such as 3D printing and streamlined hardware production, pave the way for practical deployment in real-world settings. Additionally, software-assisted data interpretation, through simplified readouts, smartphone integration, and machine learning, enables the broader use of diagnostics once limited to experts. This review explores improvements in viral diagnostic approaches, including colorimetric, optical, and electrochemical assays, showcasing their potential for democratization efforts targeting the clinic. We also examine trends such as open-source hardware, modular assay design, and standardized reporting, which collectively reduce barriers to clinical adoption and the public dissemination of information. By analyzing these interdisciplinary advances, we demonstrate how emerging technologies can mature into accessible, low-cost diagnostic tools for widespread testing. Full article

Predicting forest trail visitation is essential for sustainable management and policy development, including infrastructure planning, safety operations, and conservation. However, due to numerous informal access points and complex external influences, accurately monitoring visitor numbers remains challenging. This study applied random forest, gradient boosting, and LightGBM models with Bayesian optimization to predict daily visitor counts across six sections of the National Daegwallyeong Forest Trail, incorporating variables such as weather conditions, social media activity, COVID-19 case counts, tollgate traffic volume, and local festivals. SHAP analysis revealed that tollgate traffic volume and weekends consistently increased visitation across all sections. The impact of temperature varied by section: higher temperatures increased visitation in Kukmin Forest, whereas lower temperatures were associated with higher visitation at Seonjaryeong Peak. COVID-19 cases demonstrated negative effects across all sections. By integrating diverse variables and conducting section-level analysis, this study identified detailed visitation patterns and provided a practical basis for adaptive, section- and season-specific management strategies. These findings support flexible measures such as seasonal staffing, congestion mitigation, and real-time response systems and contribute to the advancement of data-driven regional tourism management frameworks in the context of evolving nature-based tourism demand. Full article

Background/Objectives: The economic impact of adverse events following COVID-19 immunisation (AEFIs) in Australia is underexplored. This study aimed to assess the economic burden of AEFIs on both healthcare systems and societal productivity. Methods: A cross-sectional survey was conducted in Queensland residents aged ≥18 years who had received at least one dose of a COVID-19 vaccine in the preceding 12 months. Overall, 6964 participants were recruited from July to September 2022 via email and broad social media campaigns. The survey collected data on the incidence, type and duration of AEFIs; healthcare utilisation; and work-related absenteeism. Healthcare costs were estimated using national healthcare reimbursement data, and productivity costs were estimated using Australian Bureau of Statistics Average Weekly Earnings. Results: Of the 6797 eligible respondents (predominantly female [62%]; median age: 52 years), AEFIs were reported by 53.4%, 44.1%, 40.7%, and 40.9% following doses 1 to 4, respectively. Pain and tenderness were predominant local AEFIs, while tiredness and headaches were the most frequent systemic AEFIs, generally resolving within three days. Relatively few participants reporting AEFIs consulted medical professionals: 7.0%, 7.3%, 5.1%, and 1.9% following each dose, respectively. The mean healthcare cost per person reporting AEFIs was AUD 24, AUD 88, AUD 22, and AUD 4 following each respective dose. Work absenteeism was recorded in 16.5%, 18.2%, 15.2%, and 11.2% following each dose with mean absenteeism days per person of 4.7, 7.4, 3.6 and 2.1, respectively, and mean productivity costs per person reporting AEFIs amounting to AUD 1494, AUD 2388, AUD 1136, and AUD 690, respectively. Conclusions: Participants reported mostly mild AEFIs with only a small proportion of individuals seeking medical services. Productivity costs attributable to these AEFIs exceeded direct healthcare expenses incurred. Full article

This article investigates predicted future developments in fine dining using a mixed-methods approach rooted in German gastronomic culture. By conducting an inductive media content analysis and ten semi-structured expert interviews with leading figures in Germany’s high-end food sector, we applied a qualitative mixed-methods approach. The study was based exclusively on data collected in 2018 and 2019, deliberately excluding pandemic-related developments in order to focus on long-term structural and cultural trends in fine dining. We identified two core thematic clusters: one related to sustainable food practices (ecology/sustainability, regionality, seasonality, from-farm-to-table, and vegetarianism/veganism) and the other to experiential dimensions of dining (experience, topic-based concept, and storytelling). Our findings contribute to the academic discussion on culinary futures and provide grounded insights into how fine dining is likely to evolve in response to broader societal, environmental, and cultural shifts. This study fills a significant research gap by systematically mapping emerging restaurant concepts based on non-COVID data, making it a valuable reference for scholars and practitioners alike. Full article

Public media serves as a significant source of scientific information for non-scientists. However, the simplifications and omissions inherent in media reporting often alter the nature of scientific information, potentially influencing understanding and perceptions of science and the nature of science. This study investigates how mechanistic explanations about biological processes are represented in public media, focusing on two forms of incomplete mechanistic information: “gray boxes” and “black boxes”. Using COVID-19 as a case study, we analyzed 122 media reports of biological mechanisms to understand how incomplete parts are masked by more complete explanations and their implications. Our findings highlighted three main points. First, incomplete information often appears alongside complete information within other parts of the explanation. Second, some parts of similar mechanisms are presented differently, which can create a sense of conflicting information if incompleteness is not recognized. Third, multiple filler terms are used to mask black boxes within biological explanations (e.g., “cause”, “fight”, or “mutate”). While filler terms enhance narrative flow, they can obscure gaps in scientific knowledge and lead to anthropocentric or teleological explanations. We categorized these filler terms into three groups and discussed their relevance to teaching and learning. Implications for addressing partial information in the science classroom are discussed. Full article