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The Role of Artificial Intelligence (AI) in Enhancing Accessible Experiences: Insights from Case Studies in Yorkshire
No full textChapter highlights:
• Identifies how AI can enhance accessible experiences in the domain of tourism and hospitality.
• Develops a new framework for AI-enabled accessible tourism.
• Features examples from industry practitioners of implementing AI-powered technologies to enhance accessible experiences.
• Provides a future research agenda for AI-enhanced accessible tourist experiences
AI-driven strategies for enhancing Mpox surveillance and response in Africa
Get PDFMpox, a zoonotic viral disease endemic to several African countries, has re-emerged as a significant public health concern, particularly in regions with limited healthcare resources. Current public health strategies in Africa fall short due to fragmented surveillance systems, delayed diagnostic capabilities, and inadequate resource distribution networks that cannot effectively respond to rapidly evolving outbreaks in remote and underserved areas. This narrative review explores the potential of Artificial Intelligence (AI) to enhance the management and control of Mpox in Africa. AI technologies, including machine learning and predictive analytics, can significantly improve early detection, surveillance, contact tracing, case management, public health communication, and resource allocation. AI-driven tools can analyze large datasets to identify outbreak patterns, automate contact tracing through mobile data, optimize treatment plans, and tailor public health messages to specific communities. However, the successful implementation of AI faces challenges, including limited digital infrastructure, data quality issues, ethical concerns, and the need for capacity building. Furthermore, ongoing research is essential to refine AI algorithms and develop culturally sensitive applications. This review emphasizes the need for investment in infrastructure, training, and ethical frameworks to fully integrate AI into public health systems in Africa. By addressing these challenges, AI can play a pivotal role in mitigating the impact of Mpox and enhancing the resilience of healthcare systems against future infectious disease outbreaks. This represents a novel comprehensive synthesis of AI applications specifically for African Mpox control, providing a critical framework for evidence-based implementation strategies in resource-limited settings
Sustainable digital marketing (SDM): review, taxonomy, conceptualisation and future research avenues mapping
Get PDFThe study reviews existing literature in a quest to establish an integrated taxonomy, conceptualise sustainable digital marketing (SDM), and map future research avenues. Using the scientific procedures and rationales for systematic literature review (SPAR-4-SLR) and the bibliometric analysis and theories-context-characteristics-and-methodologies (TCCM) framework, it synthesises insights from 2007 to 2024. Findings show an exponential growth in SDM scholarship, reflecting its rising academic and practical impact. Socially, the research influences sustainable product consumption, eco-friendly behaviours, corporate social responsibility (CSR), social equity, environmental education, and sustainable business models, all of which are essential to realising the United Nations (UN) Sustainable Development Goals (SDGs). Practically, businesses use SDM to improve brand reputation, gain a competitive advantage over their rivals, and ensure regulatory compliance, innovation and growth. The study unearths innovative emerging trends, development of new frameworks, impact assessment, identification of challenges and opportunities, cross disciplinary insights with significant advancement of effective professional practice and impactful theory development. It highlights how SDM integrates sustainability into marketing and technology theories (such as grounded theory, theory of social exchange, theory of planned behaviour, technology acceptance model, network systems theory, strategic orientation theory, diffusion of innovation theory, consumer culture theory, triple bottom line, sustainable development theory), advocating a forward-looking, responsibility-driven approach, proposing new frameworks, and identifying cross-disciplinary opportunities
How Creative Engagement and Innovation Drive Growth in Manufacturing SMEs in South Africa
Get PDFThis study investigates how green transformational leadership fosters creative process engagement to drive green product and process innovations, ultimately enhancing firm innovative performance and SME business growth within South Africa’s manufacturing sector. A quantitative survey was administered to 304 manufacturing SME managers in the Gauteng province, South Africa. Employing Partial Least Squares Structural Equation Modelling (PLS-SEM), the study tested hypothesised relationships among green transformational leadership, creative process engagement, green innovation (both product and process), firm innovative performance, and SME business growth, while also examining the moderating effect of top management support. Empirical results reveal that green transformational leadership significantly promotes engagement in the creative process, which drives green product and process innovations. These innovations contribute to improved firm innovative performance, positively influencing SME business growth. Moreover, top management support strengthens the relationship between innovative performance and business growth, underscoring its critical role in facilitating sustainable competitive advantage. By integrating Schumpeter’s innovation theory with the natural resource-based view, this research offers novel insights into the interplay between green leadership, creativity, and sustainable business outcomes in an emerging economy context. It fills a notable gap in the literature by focusing on South African small and medium-sized manufacturing enterprises (SMEs). It provides actionable recommendations for leaders and policymakers seeking to integrate sustainability into their strategic growth initiatives
Burden of 375 diseases and injuries, risk-attributable burden of 88 risk factors, and healthy life expectancy in 204 countries and territories, including 660 subnational locations, 1990–2023: a systematic analysis for the Global Burden of Disease Study 2023
Get PDFBackground
For more than three decades, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has provided a framework to quantify health loss due to diseases, injuries, and associated risk factors. This paper presents GBD 2023 findings on disease and injury burden and risk-attributable health loss, offering a global audit of the state of world health to inform public health priorities. This work captures the evolving landscape of health metrics across age groups, sexes, and locations, while reflecting on the remaining post-COVID-19 challenges to achieving our collective global health ambitions.
Methods
The GBD 2023 combined analysis estimated years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) for 375 diseases and injuries, and risk-attributable burden associated with 88 modifiable risk factors. Of the more than 310 000 total data sources used for all GBD 2023 (about 30% of which were new to this estimation round), more than 120 000 sources were used for estimation of disease and injury burden and 59 000 for risk factor estimation, and included vital registration systems, surveys, disease registries, and published scientific literature. Data were analysed using previously established modelling approaches, such as disease modelling meta-regression version 2.1 (DisMod-MR 2.1) and comparative risk assessment methods. Diseases and injuries were categorised into four levels on the basis of the established GBD cause hierarchy, as were risk factors using the GBD risk hierarchy. Estimates stratified by age, sex, location, and year from 1990 to 2023 were focused on disease-specific time trends over the 2010–23 period and presented as counts (to three significant figures) and age-standardised rates per 100 000 person-years (to one decimal place). For each measure, 95% uncertainty intervals [UIs] were calculated with the 2·5th and 97·5th percentile ordered values from a 250-draw distribution.
Findings
Total numbers of global DALYs grew 6·1% (95% UI 4·0–8·1), from 2·64 billion (2·46–2·86) in 2010 to 2·80 billion (2·57–3·08) in 2023, but age-standardised DALY rates, which account for population growth and ageing, decreased by 12·6% (11·0–14·1), revealing large long-term health improvements. Non-communicable diseases (NCDs) contributed 1·45 billion (1·31–1·61) global DALYs in 2010, increasing to 1·80 billion (1·63–2·03) in 2023, alongside a concurrent 4·1% (1·9–6·3) reduction in age-standardised rates. Based on DALY counts, the leading level 3 NCDs in 2023 were ischaemic heart disease (193 million [176–209] DALYs), stroke (157 million [141–172]), and diabetes (90·2 million [75·2–107]), with the largest increases in age-standardised rates since 2010 occurring for anxiety disorders (62·8% [34·0–107·5]), depressive disorders (26·3% [11·6–42·9]), and diabetes (14·9% [7·5–25·6]). Remarkable health gains were made for communicable, maternal, neonatal, and nutritional (CMNN) diseases, with DALYs falling from 874 million (837–917) in 2010 to 681 million (642–736) in 2023, and a 25·8% (22·6–28·7) reduction in age-standardised DALY rates. During the COVID-19 pandemic, DALYs due to CMNN diseases rose but returned to pre-pandemic levels by 2023. From 2010 to 2023, decreases in age-standardised rates for CMNN diseases were led by rate decreases of 49·1% (32·7–61·0) for diarrhoeal diseases, 42·9% (38·0–48·0) for HIV/AIDS, and 42·2% (23·6–56·6) for tuberculosis. Neonatal disorders and lower respiratory infections remained the leading level 3 CMNN causes globally in 2023, although both showed notable rate decreases from 2010, declining by 16·5% (10·6–22·0) and 24·8% (7·4–36·7), respectively. Injury-related age-standardised DALY rates decreased by 15·6% (10·7–19·8) over the same period. Differences in burden due to NCDs, CMNN diseases, and injuries persisted across age, sex, time, and location. Based on our risk analysis, nearly 50% (1·27 billion [1·18–1·38]) of the roughly 2·80 billion total global DALYs in 2023 were attributable to the 88 risk factors analysed in GBD. Globally, the five level 3 risk factors contributing the highest proportion of risk-attributable DALYs were high systolic blood pressure (SBP), particulate matter pollution, high fasting plasma glucose (FPG), smoking, and low birthweight and short gestation—with high SBP accounting for 8·4% (6·9–10·0) of total DALYs. Of the three overarching level 1 GBD risk factor categories—behavioural, metabolic, and environmental and occupational—risk-attributable DALYs rose between 2010 and 2023 only for metabolic risks, increasing by 30·7% (24·8–37·3); however, age-standardised DALY rates attributable to metabolic risks decreased by 6·7% (2·0–11·0) over the same period. For all but three of the 25 leading level 3 risk factors, age-standardised rates dropped between 2010 and 2023—eg, declining by 54·4% (38·7–65·3) for unsafe sanitation, 50·5% (33·3–63·1) for unsafe water source, and 45·2% (25·6–72·0) for no access to handwashing facility, and by 44·9% (37·3–53·5) for child growth failure. The three leading level 3 risk factors for which age-standardised attributable DALY rates rose were high BMI (10·5% [0·1 to 20·9]), drug use (8·4% [2·6 to 15·3]), and high FPG (6·2% [–2·7 to 15·6]; non-significant).
Interpretation
Our findings underscore the complex and dynamic nature of global health challenges. Since 2010, there have been large decreases in burden due to CMNN diseases and many environmental and behavioural risk factors, juxtaposed with sizeable increases in DALYs attributable to metabolic risk factors and NCDs in growing and ageing populations. This long-observed consequence of the global epidemiological transition was only temporarily interrupted by the COVID-19 pandemic. The substantially decreasing CMNN disease burden, despite the 2008 global financial crisis and pandemic-related disruptions, is one of the greatest collective public health successes known. However, these achievements are at risk of being reversed due to major cuts to development assistance for health globally, the effects of which will hit low-income countries with high burden the hardest. Without sustained investment in evidence-based interventions and policies, progress could stall or reverse, leading to widespread human costs and geopolitical instability. Moreover, the rising NCD burden necessitates intensified efforts to mitigate exposure to leading risk factors—eg, air pollution, smoking, and metabolic risks, such as high SBP, BMI, and FPG—including policies that promote food security, healthier diets, physical activity, and equitable and expanded access to potential treatments, such as GLP-1 receptor agonists. Decisive, coordinated action is needed to address long-standing yet growing health challenges, including depressive and anxiety disorders. Yet this can be only part of the solution. Our response to the NCD syndemic—the complex interaction of multiple health risks, social determinants, and systemic challenges—will define the future landscape of global health. To ensure human wellbeing, economic stability, and social equity, global action to sustain and advance health gains must prioritise reducing disparities by addressing socioeconomic and demographic determinants, ensuring equitable health-care access, tackling malnutrition, strengthening health systems, and improving vaccination coverage. We live in times of great opportunity
Generative AI and Job Vulnerability: A Global Review
No full textThe rapid acceleration in the development of AI-in particular, generative AI-is changing the nature of work globally, with great significant for job creation, transformation, and displacement. Drawing on major studies, employer forecasts, and AI-driven evaluations, this review paper synthesizes evidence to investigate sectoral vulnerabilities to automation across different economic contexts. In doing so, it identifies professions and sectors that have emerged as particularly vulnerable to disruption by AI, with a specific focus on developments relating to generative AI since 2023. Clerical, administrative, financial, and customer service jobs are currently identified as those globally at the highest risk, while knowledge-based and creative jobs that have been considered hitherto safe are increasingly vulnerable. Conversely, occupations that are physically and emotionally demanding and unpredictable, such as health care, skilled trades, and hospitality, remain comparatively resilient. This review also explores regional variation in risks from automation, approaches to the methodological assessment of risk, and the strategic responses from employers across industries. Conclusively, this study emphasizes a set of policy recommendations targeting concerted upskilling, AI governance, and inclusive transition strategies in efforts to prevent labor markets from becoming more unequal. This systematic literature review used information obtained from peer-reviewed journals, policy reports, and organizational datasets published between 2013 and 2025. Altogether, 52 studies were thematically analyzed and comparatively mapped across sectors in line with predetermined inclusion criteria targeted at AI-driven automation and workforce vulnerability across sectors
Interlinkages Between Financial Markets and Minerals(1990–2023): Implications for Global Energy Transitions and Environmental Sustainability
Get PDFThis paper examines the nexus between financial markets (FM), mineral resources, and global energy transitions (ET) between1990 and 2023 in the 20 leading countries exporting mineral resources, focusing on their impact on environmental sustainabil-ity. Cross-sectional ARDL, augmented mean group (AMG), and common correlated effects mean group (CCEMG) estimatorsare used for cross-sectional analysis of developed and developing countries. The findings suggest that mineral markets (MM)play a pivotal role in guaranteeing sustainable energy shifts, and FM negatively impacts the situation in the short and long term.Renewable energy consumption (REC), external investment, and digital governance (DGI) are developed countries' primarystrategies for achieving COP-26 sustainability objectives. The findings indicate that the Digital Governance Index (DGI) andFDI have varied impacts on energy transition in the economies. Developed countries enjoy the opportunities of improved andmodernised digital systems and green FDI. In contrast, developing countries must strengthen e-governance and encourage sus-tainable investments to support SDGs 7, 9, and 1
Rethinking anxiety and depression for autistic adults through personal narratives: mixed-method analysis of blog data
Get PDFBackground
Autistic adults appear to be more vulnerable to anxiety and depression than their neurotypical peers. However, definitions of emotional well-being that are suitable for autistic adults are missing from this research, along with a missing complete understanding of what contributes to and alleviates negative emotions.
Methods
Autistic adults' experiences of emotions were systematically searched for within blog data from 26 autistic authors. The search strategy identified the context of emotions, without adhering to a priori definitions. Corpus-based and thematic analyses explored the most salient contributing factors and coping responses. Consultation with autistic adults directed the research.
Results
Negative emotions were most salient and were accompanied by qualifying descriptions of being intense and misrepresented by single-word labels. The impacts of negative emotions were pain and fatigue, disrupted self-care, housing and employment, and an accumulated toll on self-identity. Emotional regulation was achieved through monitoring physiological arousal, ownership of sensory and social stressors, investing in immersive activities, planning what to expect from daily life and rejecting deficit-based views of autism.
Conclusions
The findings offer some explanation for high estimates of anxiety and depression for autistic adults, by illustrating the unsuitability of neuronormative emotion concepts and assessment tools. There are significant implications for clinical practice, insisting on a formulation of difference rather than outdated practice that foregrounds deficit and disorder. We do not suggest that these views reflect the experiences of all autistic people. This study contributes a creative and participatory method to hear the viewpoint of autistic adults. A well-being resource is shared
Exploring animal methods bias in biomedical research funding: Workshop proceedings and action steps
Get PDFNew approach methodologies (NAMs) and other nonanimal methods are increasingly effective and available to researchers for modeling human biology and disease, but barriers to their broader adoption remain. One such barrier is animal methods bias: a type of peer review bias characterized by a preference for animal-based research methods or lack of expertise to properly evaluate nonanimal methods, which affects the fair consideration of animal-free approaches. Existing evidence demonstrates that animal methods bias can affect the likelihood and timeliness of animal-free studies being accepted for publication, and anecdotes indicate that it can impact the review of applications for funding too. To assess this latter phenomenon further, the Coalition to Illuminate and Address Animal Methods Bias hosted a virtual interactive workshop in May 2024 to explore (1) how animal methods bias affects the review of grant proposals and subsequent funding rates for researchers who use nonanimal methods and (2) possible solutions for biomedical researchers and funders to mitigate these effects. Researchers, funders, peer review bias scholars, and research policy professionals gathered to synthesize current knowledge and gaps, scholarship and personal perspectives on peer review bias, and funding contexts regarding the prioritization and assessment of nonanimal research. Here, we present workshop proceedings and action steps aimed at addressing animal methods bias in funding. Possible mitigation measures include promoting the value of NAMs among the scientific community, implementing bias mitigation training, ensuring review groups have proper expertise to adequately evaluate NAMs proposals, and investing in NAMs initiatives and infrastructure
Machine learning techniques for stroke prediction: A systematic review of algorithms, datasets, and regional gaps.
Get PDFStroke is a leading cause of mortality and disability worldwide, with approximately 15 million people suffering strokes annually. Machine learning (ML) techniques have emerged as powerful tools for stroke prediction, enabling early identification of risk factors through data-driven approaches. However, the clinical utility and performance characteristics of these approaches require systematic evaluation. To systematically review and analyze ML techniques used for stroke prediction, systematically synthesize performance metrics across different prediction targets and data sources, evaluate their clinical applicability, and identify research trends focusing on patient population characteristics and stroke prevalence patterns. A systematic review was conducted following PRISMA guidelines. Five databases (Google Scholar, Lens, PubMed, ResearchGate, and Semantic Scholar) were searched for open-access publications on ML-based stroke prediction published between January 2013 and December 2024. Data were extracted on publication characteristics, datasets, ML methodologies, evaluation metrics, prediction targets (stroke occurrence vs. outcomes), data sources (EHR, imaging, biosignals), patient demographics, and stroke prevalence. Descriptive synthesis was performed due to substantial heterogeneity precluding quantitative meta-analysis. Fifty-eight studies were included, with peak publication output in 2021 (21 articles). Studies targeted three main prediction objectives: stroke occurrence prediction (n = 52, 62.7 %), stroke outcome prediction (n = 19, 22.9 %), and stroke type classification (n = 12, 14.4 %). Data sources included electronic health records (n = 48, 57.8 %), medical imaging (n = 21, 25.3 %), and biosignals (n = 14, 16.9 %). Systematic analysis revealed ensemble methods consistently achieved highest accuracies for stroke occurrence prediction (range: 90.4-97.8 %), while deep learning excelled in imaging-based applications. African populations, despite highest stroke mortality rates globally, were represented in fewer than 4 studies. ML techniques show promising results for stroke prediction. However, significant gaps exist in representation of high-risk populations and real-world clinical validation. Future research should prioritize population-specific model development and clinical implementation frameworks. [Abstract copyright: Copyright © 2025 The Authors. Published by Elsevier B.V. All rights reserved.