Search Results (348)

Vaccine hesitancy presents a significant challenge to public health, particularly in culturally diverse regions, such as South Tyrol, Italy. This article examines the interplay between political influences, conspiracy theories, and vaccine hesitancy in South Tyrol, an autonomous province characterised by its linguistic diversity and historical scepticism toward central authority. This study aimed to identify the important drivers of vaccine hesitancy and propose targeted strategies to enhance vaccine acceptance. Peer-reviewed and grey literature was examined to explore the sociocultural factors, political dynamics, and conspiracy narratives influencing vaccine hesitancy in South Tyrol. The analysis incorporated publicly available materials, including propaganda from anti-vaccine organisations, and regional public health data to contextualise the findings. Vaccine hesitancy in South Tyrol was influenced by historical tensions with the central government, cultural alignment with Austrian healthcare practices, and politically motivated opposition to vaccination. Conspiracy theories disseminated by local organisations and political entities exploit concerns regarding governmental overreach and personal autonomy. These dynamics are compounded by the selective misrepresentation of scientific discourse, which further polarises public opinion. Addressing vaccine hesitancy in South Tyrol requires culturally sensitive communication, community engagement through trusted local figures, transparency in health policies, and the proactive monitoring of misinformation. These strategies can mitigate mistrust and promote vaccine acceptance in regions with similar sociopolitical complexities. Full article

Background: Type 2 diabetes is a chronic disease with high global prevalence and significant social and economic burden. The pandemic affected patients’ diagnostics and medicines dispensing. Diabetes was among the most-affected conditions during lockdown due to the limited resources and unaffordable medicines. The impact of the pandemic on utilisation and cost has not been thoroughly studied, which inspired us to conduct the current study. Objectives: The study explored cost dynamics, changes in antidiabetic medicines utilisation, and public expenditure of pharmacotherapy in three periods: pre-pandemic (2018–2019), during the pandemic (2020–2021), and post-pandemic (2022–2023).Methods: It is a retrospective, observational, macroeconomic analysis. Reimbursed cost and utilisation were analysed as a crude sum and as indexes of the average value. Results: The result shows that five new INNs have been included in the Positive Medicines List (PML), two of these being fixed dose combinations (FDCs). During the pandemic, a slow tendency of increase of the crude sum of public expenditure was observed, followed by a sharp increase in the post-pandemic period. The public spending increased more than twice, and we found a 30,018,982 Euro growth. The highest public spending is found for dapagliflozine in post-pandemic vs. pandemic period (index = 1.67), as well as empagliflozin/metformin and dapagliflozine in pandemic vs. pre-pandemic period (index =0.21). Total utilisation increases from 58.16 to 71.78 DDD/1000 inh/day during 2018–2023. The most significant rise of utilisation is found for canagliflozin (index = 0.68) pandemic vs. pre-pandemic and dapagliflozin (index = 3.66) post-pandemic vs. pandemic. Conclusions: Analysis of the antidiabetic medicines market reveals the rising of reimbursed cost and utilisation in pre-, post-, and during the pandemic. In conclusion, organisation of the supply and financing of antidiabetic medicines was not affected during the pandemic. Full article

Research in STEAM education continues to develop, and yet the literature in this area is often theoretically ‘light’. Given the importance of theory to provide explanatory power, the aim of this paper is to offer a theoretical classification system to be used as a tool for researchers and practitioners in this area. Derived from literature searches on STEAM education within open science/schooling, secondary/tertiary education intersection and global challenges, 26 frameworks were identified from 26 academic sources and refined based on their coherence and strong theoretical heritage. A relational ontology was proposed as a recurring theme and is a foundational aspect of the final classification system, signifying a shift from traditional models of STEAM that trivialise the ‘arts’ as utility disciplines. The 26 individual frameworks are organised into four approaches: ‘experiential real-world interactions’, ‘human psychological and cognitive’, ‘social, spatial, and material interconnectivity’, and ‘cultural and equity’. The overall classification system provides a starting point for researchers and educators navigating theoretically driven yet diverse STEAM implementation and understanding its impact depending on, and in response to, their preferred methodology. This system is novel in its treatment of theoretical perspectives, and its dynamic nature allows for the inclusion of further frameworks in the future. Full article

Thyroid hormones (THs) regulate metabolism in a homeostatic state in an adult organism. During the prenatal period, prior to the establishment of homeostatic mechanisms, THs assume additional functions as key regulators of brain development. Here, we focus on reviewing the role of THs in orchestrating cellular dynamics in a developing brain. The evidence from the reviewed scientific literature suggests that the developmental roles of the hormones are predominantly mediated by non-genomic mitochondrial effects of THs due to attenuation of genomic effects of THs that antagonise non-genomic impacts. We argue that the key function of TH signalling during brain development is to orchestrate the tempo of self-organisation of neural progenitor cells. Further, evidence is provided that major neurodevelopmental consequences of hypothyroidism stem from an altered tempo of cellular self-organisation. Full article

Digital Security by Design (DSbD) is an initiative supported by the UK government aimed at transforming digital technology to deliver necessary digital resilience and prosperity across the UK. As emerging challenges in the field of digital security evolve, it becomes essential to explore how entities involved in DSbD interact and change over time. Understanding these dynamic relationships can provide crucial insights for the development and improvement of security practices. This paper presents a data-driven analysis of the evolving landscape of DSbD from 2019 to 2024, gathering insights from textual documents referencing DSbD. Using a combination of text mining techniques and network analysis, a large corpus of textual documents was examined to identify key entities, including organisations, individuals, and the relationships between them. A network was then visualised to analyse the structural connections between these entities, revealing how key concepts and actors have evolved. The results and discussion demonstrate that the network analysis offers a unique advantage in tracking and visualising these evolving relationships, providing insights into shifts in focus, emerging trends, and changes in technological adoption over time. For example, a notable finding from the analysis is the substantial increase in node relationships associated with Artificial Intelligence (AI). We hypothesise that this surge reflects the growing integration of AI into digital security strategies, driven by the need for more adaptive and autonomous solutions to tackle evolving cyber threats, as well as the rapid introduction of new AI tools to the market and their swift adoption across various industries. By mapping such connections, such results are useful, helping practitioners and researchers recognise new security demands and adjust strategies to better respond to the evolving landscape of DSbD. Full article

Increased life expectancy and reduced fertility mean more generations are living simultaneously but with fewer members. There is also a growing group of older people (aged 80 and over) who need care and support. This impacts mutual support within families and the care provided by public or private care organisations. Across OECD countries, on average, 60% of people aged 65 years and older in 2020 reported receiving support from family members, friends and people in their social network, living inside or outside their household but not care organisations. European research shows that when older persons do not have a partner (anymore), they rely on their adult children for care and support. Given that adult children frequently serve as primary providers of informal care, our study examines their perspectives and motivations to provide future care alongside the demands and expectations of their old parents. Our study adopts a multi-actor interview approach and simultaneously looks at the perspective of 40 adult children and one of their older aged parents (65 years or older). We apply the distributive justice theory to understand how children and parents assess the expectation and fairness of support. This paper contributes to the existing literature about support behaviour between parents and children, expanding insights about the fairness of support, expectations and willingness from a multi-actor approach. Through the lens of child–parent dyads, it is seen that the principles of the distributive justice theory can be perceived as not so strict, and within family relationships, one or more principles can coexist and have underlying mechanisms. This study shows the complexity and often ambivalence of family solidarity by adopting a multi-actor approach. One of the main findings is that contrasting dyads who reject the reciprocal act of support experience feelings of guilt or misunderstanding, resulting in stress and worry. A child may not follow the expected support pattern from the parent due to competing demands such as work or the prioritisation of young children, which can reduce the support given to the older parent. Besides general contrasts and similarities between child–parent support perspectives, the analysis looked into differences regarding gender and legal relationships. Our findings only found gendered care expectations. Future research should entangle this by looking into feelings of closeness, emotional connection and considering the dynamic character of filial support over time, especially between siblings. Full article

The rapid advancement of technology and increasing data utilisation have underscored the need for new models to manage and secure big data effectively. However, the constraints of isolated network environments and the limitations of existing security frameworks hinder the adoption of cutting-edge technologies such as AI and cloud computing, as well as the safe utilisation of data. To address these challenges, this study proposes an enhanced security model that integrates the concepts of Multi-Level Security (MLS) and Zero Trust (ZT). The proposed model classifies data into the following three sensitivity levels: “Classified”, “Sensitive”, and “Open”. It applies tailored security requirements and dynamic controls to each level, enhancing both data security and usability. Furthermore, the model overcomes the static access control limitations of MLS by incorporating ZT’s automated dynamic access capabilities, significantly improving responsiveness to anomalous behaviours. This study contributes to the design and evaluation of a new security model that ensures secure data protection and utilisation, even in isolated network environments such as those of military and governmental organisations. It also provides a foundation for the future development of advanced security frameworks. Full article

This article emphasises the importance of parasitological research in understanding ecological dynamics and biodiversity conservation through a global analysis of quill mites (Syringophilidae) parasitising Sunbirds (Nectariniidae). Data from 764 Sunbird individuals across seventy-six species revealed twelve quill mite species, including three newly described species: Aulonastus aethopygus Sikora and Unsoeld sp. n., Syringophiloidus haeckeli Sikora and Unsoeld sp. n., and Aulonastus arachnotherus Sikora and Unsoeld sp. n. A bipartite network analysis indicated a low connectance of 0.10, suggesting that only 10% of potential bird–parasite connections are realised. This high specialisation is further supported by an H_2′ index of 0.94 and a C score of 0.83, indicating low co-occurrence among mite species. The temperature of nestedness at 13.49 suggests a well-organised network structure. Additionally, normalised specialisation (d’) ranged from 0.60 to 1, reflecting unique host–parasite interactions. High modularity (likelihood = 0.80) with nine modules was identified, with hosts ranging from one to seven. The study concludes by discussing the host–parasite dynamics and their ecological implications within this system. Full article

Huddling behaviour is observed across various mammalian and avian species. Huddling, a behaviour wherein animals maintain close physical contact with conspecifics for warmth and social bonding, is widely documented among species in cold environments as a crucial thermoregulatory mechanism. Interestingly, on Shodoshima, Japanese macaques form exceptionally large huddling clusters, often exceeding 50 individuals, a significant deviation from the smaller groups observed in other populations (Arashyama, Katsuyama, and Taksakiyama) and climates. This study aims to uncover the mechanisms behind the formation and size of these huddling clusters, proposing that such behaviours can be explained by simple probabilistic rules influenced by environmental conditions, the current cluster size, and individual decisions. Employing a computational model developed in Netlogo, we seek to demonstrate how emergent properties like the formation and dissolution of clusters arise from collective individual actions. We investigate whether the observed differences in huddling behaviour, particularly the larger cluster sizes on Shodoshima compared to those in colder habitats, reflect variations in social tolerance and cohesion. The model incorporates factors such as environmental temperature, cluster size, and individual decision-making, offering insights into the adaptability of social behaviours under environmental pressures. The findings suggest that temperature plays a crucial role in influencing huddling behaviour, with larger clusters forming in colder climates as individuals seek warmth. However, the study also highlights the importance of joining and leaving a cluster in terms of probability in the dynamics of huddling behaviour. We discussed the large clusters on Shodoshima as a result of a combination of environmental factors and a unique social tolerance and cohesion among the macaques. This study contributes to our understanding of complex social phenomena through the lens of self-organisation, illustrating how simple local interactions can give rise to intricate social structures and behaviours. Full article

T-cell intracellular antigen 1 (TIA1) is an RNA-binding protein (RBP) that plays a multifunctional role in RNA metabolism. TIA1 has three RNA-Recognition Motifs (RRMs) and a prion-like carboxyl C-terminal domain (LCD) with intrinsically disordered regions (IDR) implicated in the dynamics (i.e., formation, assembly, and disassembly) of transient RNA-protein aggregates known as stress granules (SGs). A protein related to TIA1 is its paralog TIA1-related/like protein (TIAR/TIAL1), whose amino acid sequence, structural organisation, and molecular and cellular functions are highly conserved with TIA1. Both proteins are the main components of SGs, which are non-membranous RNA-protein condensates formed under stress to promote cell survival. Welander distal myopathy (WDM) is a late-onset muscular dystrophy that has been linked to a single-nucleotide substitution (c.1362G>A; p.E384K) in the gene encoding the TIA1 protein, which impacts TIA1-dependent SGs dynamics. Herein, we have analysed cellular and molecular aspects by targeting mutagenesis to position 384 to understand its molecular grammar in an amino acid/proteinogenic-dependent or -independent manner under oxidative stress. The observations suggest differential, even opposing, behaviours between TIA1 and TIAR in the presence of specific amino acids with negative and positive charges, and also uncharged acids, at equivalent positions of TIA1 and TIAR, respectively. Collectively, these findings illustrate a characteristic molecular grammar of TIAR- and TIA1-dependent SGs under oxidative conditions, suggesting a gain of versatility between two structurally and functionally highly conserved/related proteins. Full article

In the context of the New Normal and the VUCA (volatile, uncertain, complex, ambiguous) era, organisations face adjustments to the changes brought by the COVID-19 pandemic, especially the shifting to new work modes and configurations with their consequences on employees’ wellbeing, in terms of exhaustion, resignation, and quitting tendencies. This study, rooted in the psychology of sustainability and sustainable development, employs a primary prevention perspective to examine a relational factor, the leader–member exchange (LMX), which might shield employees from exhaustion and turnover intention. Specifically, we propose a double-chained mediation model to investigate how high-quality LMX fosters a positive adjustment to employees’ specific work modes, in-person or hybrid work, subsequently reducing employees’ feelings of exhaustion and their intentions to leave the organisation. A convenience sample of 257 Italian employees participated in this study by completing an online self-report survey. Hypotheses were tested using the PROCESS macro in SPSS 25.0 (Model 6). The results indicate that positive LMX and adjustment to the work mode reduce exhaustion and turnover intention; furthermore, they highlight the existence of a more complex dynamic linking LMX to turnover intention through a double-chained mediation of adjustment to the work mode and exhaustion. Indeed, higher LMX quality favours adjustment to the work mode, decreasing employees’ feelings of exhaustion and, in turn, their turnover intention. Specifically, the findings of this study add a novel contribution to the literature on the psychology of sustainability and sustainable development by emphasising the significance of positive LMX in becoming a factor of wellbeing and sustainability in the workplace through the promotion of the adjustment to both in-person and hybrid work modes. Organisations may benefit of this approach to LMX that, through the consideration of employees’ needs, may favour their adjustment to different work modes, thus becoming a sustainable LMX, and a promoter of employees’ wellbeing and retention. Full article

Organisations need dynamic capabilities in the ongoing digital transformation to reconfigure knowledge and learning. There is a need to define new concepts and explain mechanisms of relevant factors to build dynamic capabilities. Organisations acting in healthcare experience a dilemmatic situation. New digital processes and business models are promising benefits for cost-containment measures, improved patient-centric care, and digital services. However, investments are needed to benefit. The critical question is the following: How can individual actors in healthcare be motivated to engage in this transformational process to build and reconfigure relevant competences and establish new learning routines? Founded on the essence of the existing literature, we assume sustained learning to be a relevant dynamic capability to seize and sense competences and reconfigure human capital. This paper answers the call for deeper investigations into the mechanisms in new digitally transformed environments and sectors focussing less on performance and competitive advantages, like public administration or the healthcare sector. Based on previous research, validated in qualitative interviews and quantitative testing, we define the new construct of sustained learning with its subdimensions. By providing measures, we build the grounds for further quantitative research. Full article

Emotional intelligence (EQ) in healthcare leadership has been a subject of debate regarding its significance in enhancing job performance and patient-centred care. This systematic review investigates the impact of EQ on organisational performance metrics in healthcare leaders. Eleven studies meeting the inclusion criteria were identified through a comprehensive database search. The findings suggest that EQ positively influences job satisfaction, with emotionally intelligent leaders fostering a positive work environment and commitment among employees. Moreover, EQ correlates negatively with emotional exhaustion, indicating its potential in mitigating burnout rates among healthcare professionals. EQ fosters teamwork, organisational culture and enhances job performance, with higher EQ levels in leaders associated with increased team empowerment and proactivity. Despite the compelling evidence, limitations in the study methodologies and heterogeneity in the reported outcomes challenge the establishment of definitive conclusions. Nevertheless, the findings underscore the importance of EQ in healthcare leadership and its potential to improve organisational dynamics and employee wellbeing. This review highlights the need for further research on EQ’s impact on patient satisfaction and calls for the development of EQ training programmes tailored for healthcare leaders. Full article

The growing number of algorithmic decision-making environments, which blend machine and bounded human rationality, strengthen the need for a holistic performance assessment of such systems. Indeed, this combination amplifies the risk of local rationality, necessitating a robust evaluation framework. We propose a novel simulation-based model to quantify algorithmic interventions within organisational contexts, combining causal modelling and data science algorithms. To test our framework’s viability, we present a case study based on a bike-share system focusing on inventory balancing through crowdsourced user actions. Utilising New York’s Citi Bike service data, we highlight the frequent misalignment between incentives and their necessity. Our model examines the interaction dynamics between user and service provider rule-driven responses and algorithms predicting flow rates. This examination demonstrates why understanding these dynamics is essential for devising effective incentive policies. The study showcases how sophisticated machine learning models, with the ability to forecast underlying market demands unconstrained by historical supply issues, can cause imbalances that induce user behaviour, potentially spoiling plans without timely interventions. Our approach allows problems to surface during the design phase, potentially avoiding costly deployment errors in the joint performance of human and AI decision-makers. Full article

Social media platforms play a significant role in facilitating business decision making, especially in the context of emerging technologies. Such platforms offer a rich source of data from a global audience, which can provide organisations with insights into market trends, consumer behaviour, and attitudes towards specific technologies, as well as monitoring competitor activity. In the context of social media, such insights are conceptualised as immediate and real-time behavioural responses measured by likes, comments, and shares. To monitor such metrics, social media platforms have introduced tools that allow users to analyse and track the performance of their posts and understand their audience. However, the existing tools often overlook the impact of contextual features such as sentiment, URL inclusion, and specific word use. This paper presents a data-driven framework to identify and quantify the influence of such features on the visibility and impact of technology-related tweets. The quantitative analysis from statistical modelling reveals that certain content-based features, like the number of words and pronouns used, positively correlate with the impressions of tweets, with increases of up to 2.8%. Conversely, features such as the excessive use of hashtags, verbs, and complex sentences were found to decrease impressions significantly, with a notable reduction of 8.6% associated with tweets containing numerous trailing characters. Moreover, the study shows that tweets expressing negative sentiments tend to be more impressionable, likely due to a negativity bias that elicits stronger emotional responses and drives higher engagement and virality. Additionally, the sentiment associated with specific technologies also played a crucial role; positive sentiments linked to beneficial technologies like data science or machine learning significantly boosted impressions, while similar sentiments towards negatively viewed technologies like cyber threats reduced them. The inclusion of URLs in tweets also had a mixed impact on impressions—enhancing engagement for general technology topics, but reducing it for sensitive subjects due to potential concerns over link safety. These findings underscore the importance of a strategic approach to social media content creation, emphasising the need for businesses to align their communication strategies, such as responding to shifts in user behaviours, new demands, and emerging uncertainties, with dynamic user engagement patterns. Full article