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Podcasting, VR, AI and the Evolution of Intimacy
No full textUsing radio and podcast studies as its main scholarship areas, this paper will examine the potential for Virtual Reality (VR) and Artificial Intelligence (AI) technologies to change podcasting both in how it is crafted and in how audiences consume podcasts. Focusing on the idea of intimacy this paper interrogates how this defining characteristic of “sound-based digital media” (Hilmes, 2013:44) may persist and evolve, particularly through conceptualizations of space and place, imagined and actual. In a landscape where podcasting is increasingly perceived by (mostly younger) audiences and creators to be a visual medium (Berry, 2023), I want to explore how intimacy may continue to be a defining characteristic of podcasting. In particular, and going well beyond just the aural or indeed beyond the combination of the visual and the aural, I want to understand what intimacy can mean when podcasting moves into the virtual reality immersive space. This paper’s case study is the video version of episode 398 of the Lex Fridman Podcast, which is an interview with Facebook and Meta’s co-founder and CEO Mark Zuckerberg, titled 'First Interview in the Metaverse'. In the podcast, the two meet in the metaverse to discuss the new possibilities that this technology, in combination with AI, may open in the ways that humans communicate with each other through the internet. Looking at themes such as disembodiment, imagination and materiality, this paper examines what VR and AI technologies can mean for podcasting and the way audiences and podcasters may communicate and create meaning through them
A scoping review of cultural competence training gaps among healthcare professionals in low-and middle-income countries
Get PDFBackground: Cultural competence is an essential skill required of healthcare professionals to provide quality and inclusive healthcare, enhancing patients’ satisfaction and improving health outcomes. However, its integration in healthcare delivery remains limited, particularly in low- and middle-income countries (LMICs). Thus, this current scoping review aimed to investigate the gap in cultural competence training among healthcare professionals in LMICs. Methods: The study was guided by the methodological framework recommended by Arksey and O’Malley. A comprehensive search was conducted across six databases (PubMed, Scopus, CINAHL, PsycInfo, JSTOR, and AJOL) and non-database website (Google Scholar) to identify studies that assessed cultural competence skills or implemented cultural competence training to healthcare professionals in LMICs since the adoption of the Sustainable Development Goals (SDGs) by the United Nations in 2015 to 2nd April 2025. The search process resulted in the inclusion of thirty-eight (38) for this review. Results: Out of 2,702 studies retrieved during the literature search, 83 studies were selected for full-text review after screening, of which 38 studies were included in the final review for meeting the inclusion criteria. The included studies were conducted between 2015 and 2025 across 14 LMICs. Out of the 38 eligible studies, 27 employed quantitative method, three were conducted using qualitative method, four used a mixed-methods study approach, three employed quasi-experimental design, and one used a cluster randomised controlled trial. Cultural competence interventions were reported in only five of the 38 studies, although they were recommended across the studies reviewed. Conclusion: This scoping review highlights a critical gap between the recognition of cultural competence as a key component of quality healthcare and its integration in healthcare delivery in LMICs. Thus, there is a need to develop and implement more effective, inclusive, and contextually appropriate cultural competence training programmes for healthcare professionals in LMICs. This could significantly contribute to enhancing the cultural competence skills of these professionals, improving healthcare delivery, and enhancing patient satisfaction and health outcomes
De Gruyter Handbook of Rural Entrepreneurship in Developing Economies
No full textDespite major global shifts in agriculture, demographics, and rural environments, rural enterprise and entrepreneurship in developing economies remain underresearched, especially at the international level. This handbook addresses this gap by presenting contemporary research on rural enterprise across diverse settings, with a particular focus on Africa. It offers practical insights from countries including Egypt, Ghana, India, Kenya, Kosovo, Malaysia, Mexico, Nigeria, Tanzania, and Uganda.
Much existing policy is US or Europe-centric, often assuming superior knowledge. However, the examples discussed in this volume show that rural entrepreneurship in less developed countries may be more focused and innovative than in the Global North, especially in innovating to address climate and environmental challenges due to the immediate and visible impacts of climate change on food production and weather.
This book addresses two key questions: First, in light of the fact that entrepreneurship literature tends to be urban-centric, should "rural entrepreneurship" be a distinct category, or is it simply entrepreneurial activity in rural areas? Second, is a rural business fundamentally different from an urban one in its operations?
With its wide range of contributions and unique exploration of the definition of rural enterprise, this handbook will benefit academic scholars and postgraduate students interested in rural entrepreneurship and rural development.
Explains how and why a rural enterprise can be defined.
Addresses the lack of knowledge about rural entrepreneurship in developing economies.
Demonstrates how the rural economy underpins many developing economies from a sustainability point of view
Disease burden attributable to intimate partner violence against females and sexual violence against children in 204 countries and territories, 1990–2023: a systematic analysis for the Global Burden of Disease Study 2023
Get PDFBackground
Violence against women and against children are human rights violations with lasting harms to survivors and societies at large. Intimate partner violence (IPV) and sexual violence against children (SVAC) are two major forms of such abuse. Despite their wide-reaching effects on individual and community health, these risk factors have not been adequately prioritised as key drivers of global health burden. Comprehensive x§and reliable estimates of the comparative health burden of IPV and SVAC are urgently needed to inform investments in prevention and support for survivors at both national and global levels.
Methods
We estimated the prevalence and attributable burden of IPV among females and SVAC among males and females for 204 countries and territories, by age and sex, from 1990 to 2023, as part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2023. We searched several global databases for data on self-reported exposure to IPV and SVAC and undertook a systematic review to identify the health outcomes associated with each of these risk factors. We modelled IPV and SVAC prevalence using spatiotemporal Gaussian process regression, applying data adjustments to account for measurement heterogeneity. We employed burden-of-proof methodology to estimate relative risks for outcomes associated with IPV and SVAC. These estimates informed the calculation of population attributable fractions, which were then used to quantify disability-adjusted life-years (DALYs) attributable to each risk factor.
Findings
Globally, in 2023, we estimated that 608 million (95% uncertainty interval 518–724) females aged 15 years and older had ever been exposed to IPV, and 1·01 billion (0·764–1·48) individuals aged 15 years and older had experienced sexual violence during childhood. 18·5 million (8·74–30·0) DALYs were attributed to IPV among females and 32·2 million (16·4–52·5) DALYs were attributed to SVAC among males and females in 2023. IPV and SVAC were among the top contributors to the global disease burden in 2023, particularly among females aged 15–49 years, ranking as the fourth and fifth leading risk factors, respectively, for DALYs in this group. Among the eight health outcomes found to be associated with IPV, anxiety disorders and major depressive disorder were the leading causes of IPV-attributed DALYs, accounting for 5·43 million (–1·25 to 14·6) and 3·96 million (1·71 to 6·92) DALYs in 2023, respectively. SVAC was associated with 14 health outcomes, including mental health disorder, substance use disorder, and chronic and infectious disease outcomes. Self-harm and schizophrenia were the leading causes of SVAC-attributed burden, with SVAC accounting for 6·71 million (2·00 to 12·7) DALYs due to self-harm and 4·15 million (–1·92 to 13·1) DALYs due to schizophrenia in 2023.
Interpretation
IPV and SVAC are substantial contributors to global health burden, and their health consequences span a variety of individual health outcomes. Importantly, mental health disorders account for the greatest share of disease burden among survivors. Investing in prevention of these avoidable risk factors has the potential to avert millions of DALYs and considerable premature mortality each year. Our findings represent strong evidence for global and national leaders to elevate IPV and SVAC among public health priorities. Sustained investments are needed to prevent IPV and SVAC and to implement interventions focused on supporting the complex social and health needs of survivors
Academics’ attachment with their Institution: A collaborative autoethnographic reflection in the “newer normal” context of emergent workplace norms in higher education.
No full textComparative analysis of machine learning models for coronary artery disease prediction with optimized feature selection
Get PDFBackground
Coronary artery disease (CAD) is a major global cause of death, necessitating early, accurate prediction for better management. Traditional diagnostics are often invasive, costly, and less accessible. Machine learning (ML) offers a non-invasive alternative, but high-dimensional data and redundancy can hinder performance. This study integrates Bald Eagle Search Optimization (BESO) for feature selection to improve CAD classification using multiple ML models.
Methods
Two publicly available datasets, Framingham (4200 instances, 15 features) and Z-Alizadeh Sani (304 instances, 55 features), were used. The former predicts 10-year CAD risk, while the latter classifies current CAD status. Data preprocessing included missing value imputation, normalization, categorical encoding, and class balancing using SMOTE. We employed a 70–30 holdout validation strategy with empirical hyperparameter optimization, providing more reliable final model development than cross-validation. BESO was applied to optimize feature selection, significantly outperforming traditional methods like RFE and LASSO. Six ML models—KNN, logistic regression, SVM with linear, polynomial, and RBF kernels, and random forest—were trained and evaluated.
Results
Random Forest achieved the highest performance across both datasets. In the Framingham dataset, RF recorded 90 % accuracy, significantly outperforming traditional clinical risk scores (71–73 % accuracy). Linear models performed better on the Z-Alizadeh Sani dataset (90 % accuracy) than Framingham (66 %), indicating dataset characteristics strongly influence model efficacy.
Conclusion
BESO significantly enhances feature selection, with RF emerging as the optimal classifier (92 % accuracy) and substantially outperforming established clinical risk scores. This study highlights the potential of AI-driven CAD diagnosis, supporting early detection and improved patient outcomes. Future work should focus on prospective validation and clinical implementation
Spatial pattern and decomposition analysis of the place of residence and sexual violence among women with disabilities in sub-Saharan Africa
Get PDFBackground: Sexual violence against women is highly pervasive worldwide and remains a major public health concern. Despite the global efforts to eliminate all forms of violence against women, recent estimates revealed that approximately 1 in 3 women have experienced physical or sexual violence in their lifetime, and women with disabilities have the greatest risk of sexual violence, particularly in Africa. Thus, this study investigates the spatial pattern and decomposition analysis of the place of residence and sexual violence among women with disabilities in sub-Saharan Africa. Methods: We used the most recent secondary data from demographic health surveys, including a disability module, conducted between 2013 and 2022 in 10 sub-Saharan African countries. The study sample comprised 16,517 women with disabilities. Spatial analysis was applied to identify patterns of sexual violence, and a multivariable Blinder-Oaxaca decomposition regression analysis was used to explore the disparities between place of residence and sexual violence. The analysis took into consideration the complex survey design, with results reported in terms of percentages and adjusted coefficients. Results: The spatial pattern of sexual violence among women with disabilities varies significantly across the sub-Saharan African countries included in the study, with prevalence rates ranging from 10 to 80%. The Democratic Republic of Congo reported the highest prevalence at 23%, while Mauritania reported 2%. No cases of sexual violence were reported in Nigeria and Chad. The analysis shows that the majority of the disparity in sexual violence (72.81%) is due to differences in characteristics, with 27.19% attributed to differences in coefficients. Overall, 79.77% of women with disabilities residing in rural areas reported experiencing sexual violence. Finally, the multivariable logistics regression shows that women with disabilities who were exposed to mass media exposure were associated with lower odds of experiencing sexual violence in urban areas [aOR = 0.69*; 95%(CI 0.49–0.97), p < 0.05] but with higher odds in rural areas [aOR = 1.26**; 95%(CI 1.08–1.47), p < 0.01]. Conclusions and recommendations: The study reveals that women with disabilities in sub-Saharan Africa are vulnerable to sexual violence in both rural and urban areas, with a particularly high prevalence in rural regions. These findings are crucial for guiding the design and implementation of targeted interventions to combat sexual violence in the region
Leveraging artificial intelligence for inclusive maternity care: enhancing access for mothers with disabilities in Africa
Get PDFWomen with disabilities face significant barriers in accessing maternal healthcare, which increases their risk of adverse pregnancy outcomes, particularly in Africa, where resources are limited. Artificial intelligence (AI) presents a unique opportunity to improve inclusivity and accessibility to antenatal care, skilled birth attendance and postnatal care for these women. This paper explores the potential of AI to address the socio-economic, physical, and institutional barriers that limit the utilisation of maternal healthcare services by women with disabilities. AI-driven technologies, such as virtual assistants, predictive analytics, and wearable devices, can enhance maternal health outcomes by improving monitoring during pregnancy, providing real-time health data, and facilitating access to skilled care. However, the successful implementation of AI in maternal healthcare in Africa faces challenges, including technological infrastructure, data quality, and ethical concerns. Collaborative efforts between governments, healthcare providers, and AI developers are necessary to overcome these challenges and ensure AI tools are inclusive, culturally sensitive, and accessible. Integrating AI into maternal healthcare services could lead to improved maternal outcomes, reduce mortality rates, and promote equity for women with disabilities in Africa