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Health inequities, their causes and the role of nurses in addressing them
Get PDFThis paper presents the case for the nurses roles in promoting health equity within the UK. It commences by exploring the differences between health inequality and inequity before highlighting the health inequity experienced by individuals who are socially excluded. The paper continues by examining the nurses’ role in addressing the wider social determinants of health and promoting health in individuals from socially excluded groups
Determinants of effective participatory multi-actor climate change governance: Insights from Zambia’s environment and climate change actors
Get PDFParticipatory governance has widely been emphasised as essential to achieving SDG 13. However, recent studies have tended to focus on climate change impacts or global-level politics and governance, to the exclusion of providing practical country-level multi-actor climate governance solutions. Our study bridges this gap by examining the determinants of effective participatory multi-actor climate change governance. The objectives were to examine the current state of Zambia’s climate change governance and policy environment, to examine the elements required to actualise participatory multi-actor climate change governance, and to develop a Climate Action Coordination (CAC) Model of participatory multi-actor governance. Using semi-structured interviews with policy-level actors and a survey of implementation-level actors, we find that Zambia’s current climate governance architecture is characterised by intricate political, policy, institutional, and coordination challenges. Despite these complexities, our study reveals that effective participatory multi-actor climate change governance is contingent upon a deep understanding of the prevailing political dynamics and the effective navigation of political interference by climate actor institutions. Within such a political context, a multi-tiered governance institutional framework is essential, anchored on both an influential political authority and robust multi-level technical autonomy. Our results also identify various determinants such as: broad stakeholder inclusion; clarity of roles; decentralisation of decision making, with safeguards to limit policy reversals; harnessing of indigenous knowledge; alignment to the broader national development agenda; adequate financing; leveraging the influence of global commitments; and establishing parliamentary oversight mechanisms, among others. We synthesised these determinants into a practical CAC Model that cuts across the different administrative and sectoral tiers of climate change governance. Our study is unique as it offers a broad, multifaceted, and practical consideration of the determinants of climate change governance. This is particularly useful for a country like Zambia that has embarked on ambitious environmental and climate change sector reforms
A Statistical Analysis of the Non-Newtonian Nanofluid Flow Model in a Single-Phase Squeezing Channel
No full textThe paper aims to study the flow and heat transfer characteristics of non-Newtonian Maxwell nanofluid passing through a squeezing channel in the presence of thermal radiation. The system of partial differential equations governing the flow problem are transformed into a set of dimensionless ordinary differential equations with the help of similarity variables. The approximate numerical solution is computed using shooting technique along with 4 th order Runge-Kutta method. The impact of several physical parameters on the nanofluid velocity, temperature, the coefficient of skin friction, and heat transfer rate are also explored. Further, a multivariate quadratic regression analysis for skin friction coefficient and Nusselt number against the key parameter is also performed and expressions for the same is presented. The findings reported from the study suggests that the temperature of non-Newtonian graphene based nanofluid enhances with increasing strengths of magnetic field, nanoparticle volume fraction and Joule dissipation whereas the opposite response in nanofluid temperature is observed for thermal radiation and Deborah number. The findings of present research may have bearings in the fields of biomedical engineering, automobiles, powder technology and high-energy devices. The findings indicate that as the nanoparticle volume fraction increases, there is a corresponding increase in both skin friction and Nusselt number values
Impact of Interoperable Technologies on Financial Inclusion of Smallholder Farmers in Pakistan
No full textFinancial institutions face persistent challenges in lending to smallholder farmers due to supply-side barriers such as limited outreach, information asymmetry, high operational costs, and restrictive eligibility criteria. Interoperable technologies, which is the ability of different systems, devices, or applications to communicate and work together effectively, provide promising solutions, fostering integration of lending systems and improved service delivery. However, little is known about how these technologies affect financial institutions’ operational and strategic behaviour. To address this gap, this study employs a case study approach to analyse how interoperable technologies influences the lending processes, operational strategies, and credit disbursement mechanisms of a group of financial institutions participating in a government-supported rural microcredit programme in Punjab, Pakistan (‘e-Credit’). By focusing on the ‘e-Credit’ initiative as a critical case study, the research provides an in-depth exploration of the adoption and impact of interoperable technologies within a real-world context. This approach enables this research to generate rich, context-specific insights into the challenges and opportunities of interoperable technologies adoption, offering valuable lessons for policymakers, practitioners, and researchers. Drawing on semi-structured interviews with stakeholders in ‘e-Credit’, this research investigates how interoperable technologies addresses supply-side barriers faced by a range of financial institutions in lending to smallholder farmers. The findings reveal that interoperable technologies enhance outreach, efficiency, and service quality through improved accuracy, reliability, and transparency in delivery, while also deepening usage by enabling more diverse and frequent financial interactions. However, adoption also poses constraints, including high implementation costs, lagging regulatory frameworks, and resistance from financial institutions staff resulting in marginalised financial services products for smallholder farmers. While interoperable technologies show transformative potential in reshaping financial institutions’ strategies, this research highlights the need for aligned policies, practice, and demand-side interventions, offering actionable insights for advancing digital financial inclusion
COVID‐19 Persian Misinformation Detection on Instagram: A Comparative Analysis of Machine Learning and Deep Learning Methods
Get PDFThe proliferation of misinformation on social media platforms like Instagram poses a significant threat, eroding public trust and potentially endangering public health. While research in this area has grown considerably, a notable gap remains in addressing misinformation in languages other than English. This study bridges that gap by establishing a comprehensive performance benchmark for detecting misinformation on Instagram, focusing specifically on Persian‐language content related to COVID‐19. To support this research, we constructed a novel labelled dataset of 27,000 Persian comments collected from Instagram. We employed five established machine learning and deep learning approaches for misinformation analysis: XGBoost (with TF‐IDF embedding), LSTM (with GloVe embedding), CNN, KNN and BERT. To prevent overfitting, we conducted model training and validation, evaluating performance using confusion matrices. The results demonstrate that the LSTM model achieved the best classification performance, with an accuracy of 0.97, precision of 0.91, recall of 0.85 and an F1‐score of 0.85. This outcome establishes the first high‐performance baseline for deep learning methods on this unique Persian misinformation dataset. Furthermore, the training and validation curves for the LSTM approach indicate its effectiveness in mitigating overfitting
Perceived bioethical issues in cancer rehabilitation: a qualitative study among Italian physiotherapists
Get PDFIntroduction: Literature on bioethics in physiotherapy, particularly in cancer management, is limited. This study explores the perceived bioethical issues in cancer rehabilitation by Italian physiotherapists.Participants: Thirty-one physiotherapists (Age: 42 ± 10.5 years; 20 women, 11 men) with expertise in cancer rehabilitation were purposefully selected.Data Collection: Six online focus groups were conducted, guided by a focus group guide based on existing literature and refined by experts in cancer rehabilitation and bioethics.Data Analysis: Sessions were recorded, transcribed, and analyzed using Braun and Clarke’s ‘Reflexive Thematic Analysis’.Results: Four primary themes emerged: 1) Challenges of (Non)-Disclosure in Diagnosis and Prognosis – ethical difficulties around withholding diagnosis or prognosis information; 2) Balancing Hope and Realism in Patient and Caregiver Expectations – navigating hope versus realistic rehabilitation goals; 3) Weighing Efficacy and Safety in Cancer Rehabilitation – balancing treatment outcomes with patient safety; 4) Decisions on Withdrawing Treatment – ethical considerations in discontinuing treatment.Discussion: These themes highlight common ethical dilemmas faced by physiotherapists in cancer rehabilitation, mirroring broader healthcare challenges. Addressing them requires a nuanced understanding of ethical principles within the cancer rehabilitation context.Conclusions: The study provides insights into the bioethical issues in cancer rehabilitation, stressing the need for a patient-centered approach to navigate these challenges effectively
The Assessment of SpondyloArthritis International Society (ASAS) Consensus-Based Expert Definition of Difficult-to-Manage, including Treatment-Refractory, Axial Spondyloarthritis.
Get PDFTo develop a consensus-based expert definition of difficult-to-manage (D2M) axial spondyloarthritis (axSpA), incorporating treatment-refractory (TR) disease. A literature review was conducted in 2022 to identify potential definitions for D2M/TR axSpA from prior studies, followed by a 2-round Delphi consensus process conducted in 2022 and 2023 to identify components of D2M axSpA. Based on the results of the Delphi process, a draft of the D2M axSpA definition was developed and presented to the expert task force, including patient representation, and, subsequently, to the Assessment of SpondyloArthritis International Society (ASAS) membership for endorsement in January 2024. Consensus was reached on a D2M definition encapsulating treatment failure (treatment according to the ASAS-European Alliance of Associations for Rheumatology recommendations and failure of ≥2 biological or targeted synthetic disease-modifying antirheumatic drugs with different mechanisms of action unless contraindicated), suboptimal disease control, and physician or patient acknowledgement of problematic signs/symptoms in patients diagnosed with axSpA by the rheumatologist. This definition represents a broad concept that includes various reasons that lead to an unsatisfactory treatment outcome. TR axSpA is covered by the D2M definition but requires a history of treatment failure, the presence of objective signs of inflammatory activity, and the exclusion of noninflammatory reasons for nonresponse. The proposed D2M definition incorporating TR disease was endorsed by ASAS at the annual meeting in January 2024, with 89% votes (109/123) in favour of it. The ASAS D2M axSpA definition, including TR disease, allows for identifying patients with unmet needs, paving the way for further research in this condition and its clinical care improvement. [Abstract copyright: Copyright © 2025 The Author(s). Published by Elsevier B.V. All rights reserved.
A Review on Blood Flow Simulation in Stenotically Diseased Arteries.
No full textHeart diseases which can lead to stroke and heart attacks, affect numerous individuals worldwide due to disruptions in blood flow within the body. A common underlying cause for such hemodynamic disorders is a constriction in the artery, which is known as a stenosis, which is attributable to a range of causes including atherosclerosis or plaque accumulation. Many theoretical and computational studies have been presented in this area providing a useful compliment to experimental (clinical) studies. These studies have benefited clinical practice by providing insights into complex fluid dynamics associated with cardiovascular disease and identifying robust methodologies for mitigating such diseases. This review therefore aims to provide an overview of recent mathematical and numerical modelling advancements in understanding blood flow in stenosed arteries which have served to expand the current understanding of disease onset and mitigation for patients. Many diverse aspects of stenotic hemodynamics have been addressed in a large body of literature under various assumptions, such as different fluid material models, artery channel characteristics and diverse analytical and numerical solution techniques. These studies have also considered a variety of multi-physical effects including heat transfer, mass diffusion, nanoparticle effects in actual clinical treatments. In this review, over 100 recent articles from reputable journals are appraised. The primary objectives of this review paper are to emphasize the methodologies used for modelling, numerical simulation, and robust evaluation of hemodynamic characteristics in arterial blood flow which provide a more sophisticated insight into hemodynamics associated with diseases and possible mitigation strategies. The tabular format outlines different aspects of geometries and blood behavior (fluids) examined in the period 2015-2025. This organized presentation and crystallization of key contributions in a single article will also serve as a valuable resource for multi-disciplinary researchers including mathematicians, bioengineers, computer scientists in addition to medical researchers. Future pathways are also outlined
Enhancing Infrared Small Target Detection: A Saliency-Guided Multi-Task Learning Approach
Get PDFObject detection in infrared images poses a considerable challenge due to its small-scale targets, low contrast and poor signal-to-clutter ratio, often resulting in a high false alarm rate. To improve the detection accuracy on infrared small targets, we introduce Light-SGMTLM, a lightweight and saliency-guided multi-task learning model. This model integrates saliency detection into the YOLOv5x framework through a parallel multi-task learning structure and employs a joint loss function during training. Such integration significantly alleviates the impact of complex backgrounds and improves the precision of small target localization. Moreover, we have developed a streamlined module, termed SIWD, to create a more agile backbone, which establishes an optimal balance between precision and efficiency, making the model more suitable for situations with limited computational resources. Comprehensive comparative experiments were conducted on six infrared small target datasets, namely, Small-ExtIRShip, Small-SSDD, IHAST, NUAA-SIRST, IRSTD-1k, and IRDST, and we assessed the model’s performance against ten leading target detection models, such as YOLOv7, YOLOv8, DINO, and Relation-DETR. The findings reveal that our method’s unique joint learning architecture, combining saliency and object detection tasks, significantly improves accuracy for infrared small target detection. Notably, it achieved impressive mean average precision (mAP) values of 92.60% and 75.71% on the NUAA-SIRST and IRSTD-1k datasets, respectively
The Association of Urban Greenspace Characteristics with Tick Densities and Borrelia burgdorferi prevalence in Scotland
Get PDFTicks are an ecosystem disservice in urban greenspaces, with the potential to transmit diseases. The characteristics of an urban greenspace can impact the hazard of ticks and tick-borne pathogens both within a greenspace and in the surrounding area. This research aimed to understand how the configuration, connectivity, area, and land cover of urban greenspaces can influence the population densities of ticks and the associated hazard of Borrelia burgdorferi, the agent of Lyme disease. Tick densities were estimated at 34 sites across Scotland in 2022 and 2023, and tick samples were analysed to detect the prevalence of B. burgdorferi pathogens. The area and connectivity of each greenspace was calculated, as well as the proportions of four land cover types within a 1 km buffer around each greenspace. An agent-based model was used to explore how the configurations of single large vs several small greenspaces may influence the risk of tick bites and Borrelia infections. Increased connectivity of urban greenspaces was significantly correlated with increased density of nymphs (DON) and the density of infected nymphs (DIN) within greenspaces. Increased greenspace area was associated with increased DIN, but not DON. Land cover was found to have varying effects on DON and DIN; Increased woodland cover was associated with increased DIN but decreased DON. The proportion of built-up area was negatively associated with the DIN. Increased areas of improved grassland were associated with increased DIN, while the proportion of semi-natural grassland had the opposite effect. Modelling outputs suggested that while the risk of tick bites may be significantly higher in a ‘several small’ greenspace configuration, the risk of Borrelia infections is significantly higher in a ‘single large’ greenspace. These results highlight the need for urban planners to recognise these potential disservices when designing greenspaces, and the importance of educating the public about tick awareness
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