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Moving forward: the introduction of specialist cardiovascular nurses in Zambia
No full textThe concept of universal health coverage requires that all people have access to a continuum of essential healthcare services, including health promotion, prevention, treatment, rehabilitation and palliative care, without financial hardship (World Health Organization (WHO), (2025). Nurses are key to achieving this because they are found at every step of the patient journey
Measuring digital advertising in a post-cookie era: A study of marketing-mix models, attribution and incrementality
No full textThe implementation of the EU General Data Protection Regulation (GDPR) poses significant challenges to the measurement of advertisement performance, necessitating a shift away from traditional tracking methods such as web beacons, cookies and browser fingerprinting. With the discontinuation of thirdparty cookies and increased privacy standards, it has become harder for marketers to optimise advertising spend and attribution models. This paper explores the GDPR’s impact on the ad tech industry and anticipates challenges in adapting to a cookieless and more regulated data environment. In the wake of iPhone Operating System (iOS) updates and app tracking transparency, direct-to-consumer (D2C) companies have witnessed shifts in advertising spending across different channels. The study investigates three main measurement models: marketing-mix modelling (MMM), multi-touch attribution (MTA) and incrementality for D2C marketers, highlighting incrementality as the most effective method for analysing advertisement impact and optimising spending. The key contributions include a proposed triangulation framework that combines data from MMM, MTA and incrementality to support a datadriven approach, offering insights for tactical and strategic decision-making. To validate the proposed framework, a mixed-methods approach involving qualitative and quantitative surveys is designed. Targeting experienced advertising professionals, the survey evaluates the implementation of MMM and incrementality, assessing the various decision-making attributes of measurement models, such as easeof- use, accuracy, validation, robustness, predictiveness etc. Results align with existing literature and the proposed framework, demonstrating the efficiency of each technique. The paper recommends adoption of the incrementality randomised control trial method and provides a roadmap for further research in this evolving landscape
How environmental management accounting drives performance: a meta-analysis considering national EMA maturity
No full textPurpose
This study investigates the impact of environmental management accounting (EMA) on organizational performance, with a focus on how national EMA maturity, performance type and firm size influence this relationship. The aim is to explore how EMA can support sustainability goals while enhancing performance across diverse contexts.
Design/methodology/approach
A comprehensive meta-analysis was conducted, incorporating 36 studies with a combined total of 13,010 observations. Data from the Future of Growth Report (2024) by the World Economic Forum were used to create an innovative EMA index that classifies countries based on their level of EMA adoption. It explores how the EMA–performance relationship varies across national, organizational and performance-specific factors.
Findings
The meta-analysis confirms EMA’s positive impact on performance, moderated by national EMA maturity, performance type and firm size. High-maturity contexts and large firms see more significant benefits, with environmental performance showing the strongest link. These insights underscore EMA’s role in driving performance while highlighting the need for context-specific strategies, especially in less developed EMA environments or for small and medium-sized enterprises (SMEs).
Practical implications
Organizations in high EMA maturity countries or larger firms should adopt EMA to boost environmental performance, while policymakers should improve EMA frameworks in less developed regions and support SMEs with resources. Additionally, companies should prioritize EMA to enhance sustainability, given its strong impact on environmental outcomes.
Originality/value
This study enriches EMA literature by analyzing how national context, firm size and performance type affect the EMA–performance link, offering practical insights for aligning sustainability and performance goals for researchers, practitioners and policymakers
Digital twins for the era of personalized surgery
No full textDigital twins can aid surgeons in training and in performing interventions with greater awareness and precision. The range and variety of digital twins in surgery are described, and their use across perioperative care is discussed. While largely experimental, they are beginning to show promise for the enhancement of personalized, adaptive, and data-driven surgical care. Issues relevant to the greater adoption and deployment of digital twins are all considered
Sustainable Construction Logistics: Analysing Challenges, Solutions, and Critical Success Factors in the Middle Eastern Private Sector
No full textPurpose
The construction industry in the Middle East faces significant logistics challenges that negatively impact sustainability, leading to substantial waste, project delays, and cost overruns. However, there is limited understanding of inefficient logistics practices and their consequences within the region. Addressing these challenges is essential for minimising the environmental impact and enhancing competitive advantage. This study focuses on Saudi Arabia, the United Arab Emirates, Jordan, and Palestine, as these countries represent key construction hubs in the Middle East, each facing unique logistical challenges and opportunities for improvement. By examining the causes and consequences of logistics inefficiencies on sustainability within these countries, this study aims to develop a practical model that incorporates logistics challenges, solutions, and critical success factors towards enhancing sustainability.
Design/methodology/approach.
This study follows a sequential explanatory mixed-methods approach within deductive reasoning and a critical realist philosophy. This study begins with a literature review to establish a theoretical foundation and develop a conceptual model. This was followed by semi-structured interviews with 29 construction logistics experts to enhance their understanding, gain industry insights, and identify emerging issues. In the final phase, a quantitative survey was conducted with 422 industry stakeholders to test the proposed model and empirically validate the findings. This approach ensures a rigorous assessment of theoretical assumptions while capturing industry-specific challenges and solutions in construction logistics in the Middle East.
Findings
During the semi-structured interviews, the significance of logistics in Middle Eastern construction was emphasised and challenges were identified, including two unique to the region: sudden labour shortages and unstable conditions. This study revealed that contractors in the Middle East lag in adopting logistics solutions and critical success factors. The conceptual model, developed through literature and interviews, incorporated twenty-three logistics challenges, seven critical success factors, seventeen logistics solutions, and seven sustainability factors. This finding contributes significantly to construction logistics research by using structural equation modelling (SEM) to examine and validate the theoretical model. The model aims to address logistics challenges by integrating solutions with key success factors to enhance sustainability in the Middle East's construction sector. SEM provides a robust statistical methodology for examining complex variable relationships and evaluating direct and indirect effects while accounting for multiple interactions. Moderation analysis revealed that project country, type, and location influenced relationships between logistics challenges, solutions, and sustainability, highlighting the need for tailored approaches. Additionally, logistics challenges mediate between logistics solutions, critical success factors, and sustainability, underscoring their critical roles in achieving sustainable outcomes in construction projects.
Research Limitations/Implications.
The focus of this research is to improve the logistics of construction, leaving aside the control aspects. A literature review was conducted using a trusted English database that covered publications from 2000 onwards. This study acknowledges that the synthesis of diverse research is challenging. The staff members of contractors in the private sector of the Middle East were targeted for primary data collection. Potential improvements can be achieved by including the perspectives of both clients and donors. In Saudi Arabia, the UAE, Jordan, and Palestine, questionnaires were given face-to-face, but data collection was constrained in Iraq, Syria, and Lebanon due to instability.
Originality/Contribution
This study contributes theoretically by integrating existing frameworks to analyse logistics challenges and their impact on sustainability, emphasising external and regional influences while incorporating solutions and critical success factors. It provides Middle Eastern construction firms with a tailored model to enhance operational sustainability, offering actionable strategies for contractors, engineers, and policymakers. Methodologically, it employs semi-structured interviews for context insights and uses structural equation modelling (SEM) to validate the framework, enabling an evidence-based approach to addressing logistics challenges and promoting sustainability
Competitive Match Running Speed Demands and Impact of Changing the Head Coach in Non-League Professional Football
No full textMatch running speed demands vary across competitive levels of football, influenced by player position, tactical considerations, and Head Coach changes. In England, the level directly below professional football, Non-League Football (NLF), comprises full-time and part-time clubs. However, the running speed demands of professional teams at this level remain unknown. Therefore, this study aimed to investigate (1) the match running speed demands in a professional NLF team, and (2) the impact of changing the Head Coach on these physical demands. Match running speed data were collected via Polar Team Pro global positioning system (GPS) devices during 41 matches of a tier 6 NLF team, comprising 311 observations of 22 full-time outfield players. Linear mixed-effect models examined the relationship between running speed metrics and fixed effects of a Head Coach change (n = 3), player position, and match outcome, with match number as a random effect. The team average total distance (TD) was 10,479 ± 42 m, and high-speed running and sprinting were 431 ± 62 m and 99 ± 26 m, respectively. The results showed significant positional differences, with wide defenders and midfielders associated with a greater TD than central defenders and strikers. Moreover, a change in Head Coach was significantly associated with a reduced TD, and a similar downward trend was observed across other running speed metrics. The TD and positional differences observed are comparable with other football cohorts, yet HSR and sprinting distances were notably lower in professional NLF. The findings highlight NLF clubs’ challenges in transitioning to higher competitive levels and provide insights for performance and training. Further research is warranted to explore the influence of running speed demands, technical and tactical factors, and other determinants on success in NLF
Data Decomposition Methods for Medical Image Classification
No full textAlthough deep learning methods have achieved outstanding success in the medical image field, they face several challenges that can significantly impact training effectiveness, learning stability and meaningful generalisations. One of these challenges is the limited availability of annotated samples for certain diseases, which are often difficult and expensive to obtain. Another important issue is the presence of overlapping class distributions, where similarities between features of different classes make it difficult for the model to distinguish between them accurately.
To address these issues, the thesis aims to develop deep learning solutions that improve classification performance when faced with limited annotations and irregular class distributions. The study specifically focuses on three key objectives: (1) designing a convolutional neural network that improves feature learning from a generic domain to a more specific task with small annotated samples, (2) developing a deep learning model that effectively mitigates class overlap by refining class boundaries, and (3) enhancing the generalisation strategy to improve learning stability and simplify complex patterns within datasets.
To achieve these objectives, the thesis presents three main contributions. First, the 4S-DT model and its advanced version, XDecompo, are introduced to enhance feature transferability through self-supervised learning with sample decomposition and overcome the limited samples of the dataset. 4S-DT uses the k-means clustering to perform the decomposition process, which may not always align with the true structure of the data. In contrast, XDecompo employs an affinity propagation-based class decomposition to automatically enhance the learning of the class boundaries in the downstream task without the need for preset cluster numbers. This clustering process provides more flexibility and adaptability compared to the parametric approach used by 4S-DT. Moreover, XDecompo also incorporates an explainable component to highlight salient pixels that influenced the model’s decision and explain the effectiveness of XDecompo to enhance the feature extraction and increase the trust of deep learning applications.
The second contribution introduces CLOG-CD, a convolutional neural network that integrates curriculum learning with class decomposition to improve classification performance on medical image datasets exhibiting class irregularities. CLOG-CD also explores different oscillation steps to evaluate the impact of varying learning speeds on model generalisation at different levels of granularity.
The third contribution of the thesis introduces a novel curriculum learning with a progressive of self-supervised learning called (CURVETE) that employs a curriculum learning strategy based on the granularity of sample decomposition during the training of unlabelled samples. Through this process, CURVETE enhances the quality of feature representations, extracting rich information across different levels of granularity. These features can then be effectively transferred to a new downstream task with limited examples, ultimately improving classification performance. CURVETE also handles the challenge of irregular class distribution by utilising the curriculum learning strategy with a class decomposition approach in the downstream task.
Extensive experiments have been carried out on various medical image datasets, utilising different evaluation metrics, to validate the effectiveness of our three contributions to the thesis. For the first contribution, 4S-DT has achieved a high accuracy of 97.54% and 99.80% for detecting COVID-19 cases in dataset-A and dataset-B, respectively. Additionally, XDecompo achieved accuracies of 96.16% and 94.30% for colorectal cancer and brain tumour images, respectively, outperforming 4S-DT and other training strategies. The second contribution, CLOG-CD, achieved an accuracy of 96.08% on the chest x-ray dataset, 96.91% on the brain tumour dataset, 79.76% on the digital knee x-ray, and 99.17% on the colorectal cancer dataset using the baseline ResNet-50. In addition, CLOG-CD using DenseNet-121 achieved 94.86%, 94.63%, 76.19%, and 99.45% for chest x-ray, brain tumour, digital knee x-ray, and colorectal cancer datasets, respectively. Finally, CURVETE showed significant improvements in performance with an accuracy of 96.60% on the brain tumour dataset, 75.60% on the digital knee x-ray dataset, and 93.35% on the Mini-DDSM dataset using the baseline ResNet-50. Furthermore, the classification performance with the baseline DenseNet-121 achieved an accuracy of 95.77%, 80.36%, and 93.22% on the brain tumour, digital knee x-ray, and Mini-DDSM datasets, respectively
Experiences and Perceptions of Academic Motivation in Adolescents With a Refugee Background: A Reflexive Thematic Analysis
No full textLittle previous research exists on academic motivation in refugee adolescents, and none has been conducted in the UK that might help educators to promote motivation and mitigate demotivation in the young people they support. The aim of this study is to help address this gap by exploring experiences and perceptions of academic motivation in refugee adolescents settled in the UK. Semi‐structured interviews were conducted in person or online with three refugee adolescents and six key informants who support the education of refugee adolescents. Data was interpreted by reflexive thematic analysis, which generated three themes: refugee adolescents are striving for stability and security; academic motivation is affected by social and academic relationships; and refugee adolescents are unique individuals with varied educational needs. Of particular note, positive social and academic relationships were found to be motivating, whereas instability in refugee adolescents' lives and negative interactions with teachers were demotivating. The findings also highlight the importance of recognising refugee adolescents' individuality and their unique characteristics, which inform their educational needs and academic motivation
Learning about learning: developing conservatoire students’ pedagogical knowledge
No full textConservatoires are specialist and practical schools that provide immersive training for aspiring performing arts professionals. Historically, performance training in music has been valued over and above other aspects of the curriculum in conservatoires. For example, learning how to teach is rarely considered as important as learning how to perform. Yet pedagogical training in music has enormous potential to support students to learn about their own learning development and that of others, and as such can enhance students’ employability in or beyond music. Whilst this article focuses on the discipline of music, it poses important questions about the value for students of learning about learning via pedagogical training in many other creative subject areas, such as dance, drama, art, and languages, or even STEM subjects (sciences, technology, engineering, and mathematics). Developing students’ metacognitive awareness may benefit society, helping students to nurture the next generation of learners, whilst impacting positively on longer-term recruitment into conservatoires and other Higher Education Institutions