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Investigating barriers and facilitators to engagement with the National Breast Screening Programme among women in Malta: A systematic review
Full text linkObjective: This systematic review aimed to identify the barriers and facilitators affecting engagement with Malta’s National Breast Screening Programme (NBSP) among women aged 50-69, focusing on studies published over the past decade.
Methods: A comprehensive search of PsycINFO, PsychArticles, MEDLINE, and Google Scholar was conducted in June 2024, resulting in 48 records. Five full-text papers met the inclusion criteria, comprising English-language studies with quantitative, qualitative or mixed-method designs that examined breast screening engagement in Malta. Independent risk of bias and quality assessments were conducted using the Joanna Briggs Institute checklist for cross-sectional studies.
Results: The review analysed data from 911 women, with a mean age range of 54.6-58.0 years. Barriers and facilitators to NBSP participation were categorised into three levels: psychological (knowledge gaps, fear and anxiety, health beliefs and illness perceptions), social (educational level, income and marital status), and healthcare-related (patient satisfaction and accessibility).
Conclusions: The findings indicate that targeted educational initiatives, improved accessibility, and enhanced patient support could significantly boost screening uptake, leading to earlier breast cancer detection, reduced mortality rates, and better health outcomes for women in Malta.
Conclusion: These insights are not only valuable for refining breast screening programmes across Europe, but can also inform similar initiatives globally, particularly in countries with comparable barriers and healthcare challenges. Adopting these strategies in diverse contexts may help improve women’s health outcomes worldwide
Psychometric validation of the simplified Chinese version of the dyspnoea-12 questionnaire for patients with primary lung cancer
Full text linkPurpose: The simplified Chinese version of the Dyspnoea-12 Questionnaire (D-12) has not yet been translated and validated for patients with primary lung cancer. This study aimed to evaluate the psychometric properties of the simplified Chinese version of the D-12 for patients with primary lung cancer. Methods: This study analysed the baseline data of a randomised controlled trial that used an inspiratory muscle training intervention for patients with thoracic malignancies. The original English version of the D-12 was translated into simplified Chinese according to standard instrument translation and adaptation procedures. The internal consistency reliability of the D-12 was determined by calculating Cronbach’s alpha coefficients. The convergent validity of the D-12 was evaluated by Spearman’s correlation with the Borg CR-10 Scale, Numerical Rating Scale (NRS), Hospital Anxiety and Depression Scale (HADS), and Saint George’s Respiratory Questionnaire (SGRQ). Blood oxygen level, the 6-minute walk test distance, alcohol use, surgery type, cancer stage, exercise level, and educational background were identified to evaluate their discriminating performance. Results: The analysis included 196 participants. The Cronbach’s alpha coefficients for the full D-12 and its physical and emotional function subscales were 0.83, 0.74, and 0.92, respectively. Significantly positive associations were found between the D-12 scores and the Borg CR-10 Scale, the NRS, the HADS, and SGRQ scores (p < 0.01). The participants with insomnia (p < 0.01) and who did not use alcohol (p = 0.019) reported significantly higher D-12 total scores compared with their respective counterparts. The participants at different cancer stages (p < 0.01) and those who had undergone different surgeries (p = 0.033) reported significantly different D-12 total scores. Conclusions: The D-12 simplified Chinese version demonstrated very good psychometric properties and high acceptability in patients with primary lung cancer
Determining the outcome measures and clinical relevance of respiratory muscle training with multiple sclerosis patients: a systematic review
Full text linkThe following systematic review aimed to gather information on the effectiveness of Respiratory Muscle Training (RMT) with Multiple Sclerosis (MS) patients. The method followed the ENTREQ and PRISMA protocol. MEDLINE, Cochrane, and Science Direct databases were used to source relevant literature. Articles included participants diagnosed with MS in randomized, controlled trial studies with objectively measured outcomes, and RMT methods were standardized. Eleven students were included in the results (n = 396, 50.5 ± 9.8 years, 68% F 31% M) and show that RMT (minimum 8 weeks of training) is effective in improving respiratory muscle strength (MIP in 7 out of 9 studies, MEP in 6 out of 11 studies and FVC in 6 out of 7 studies) and health-related outcomes, including mobility. Although muscle strength increased, increases in FVC had moderate effects on functional ability, which were negligible, and patient-reported fatigue. Findings suggest that muscle strength increases were predominantly in inspiratory muscles, and expiratory results were combined. However, the review shows a lack of research concerning the use of RMT and its prescription for MS patients
Dynamic Capabilities Theory : A review
Full text linkDynamic capabilities (DCs) are higher-level competences enabling organisations to integrate, build, and reconfigure resources to address and shape dynamic environments
Inspiratory muscle training: A theoretical framework for its selected application in orthopaedic enhancing recovery pathways
Full text linkThis paper explores a theoretical framework for integrating Inspiratory Muscle Training (IMT) into enhanced recovery pathways, emphasising its potential role in mitigating respiratory decline, reducing hospital stays, and improving functional mobility for selected patients. IMT has shown benefits in high-risk surgical populations, including those with chronic respiratory conditions, obesity, obstructive sleep apnea, and frailty. Standardised screening protocols involving respiratory muscle function tests are recommended to identify suitable candidates, with structured IMT programs ideally commencing 6–8 weeks before surgery. Implementing IMT within an enhanced recovery pathway may enhance the ability for early mobilisation, improve oxygenation, and support the functional recovery of patients. While IMT has demonstrated efficacy in various surgical populations, its specific benefits to orthopaedic patients require further consideration and investigation. Indeed, future research should focus on optimising IMT protocols and assessing patient outcomes in the short-term (e.g. length of stay and complications), and the medium-term (e.g. return to activities of daily living). By incorporating IMT into prehabilitation and rehabilitation protocols, we propose that healthcare systems may be able to improve surgical outcomes and patients’ well-being while reducing postoperative complications and healthcare burden for at-risk patients
Building a bridge over turbulent waters: An equality impact assessment co-production approach to developing an environmental justice framework for the UK and beyond
Full text linkThere is increasing recognition that the environmental crisis places disproportionate burdens on already marginalised communities. It is also increasingly clear that environmental sustainability policies can increase inequality if not accompanied by broader policy measures to address inequalities. To seek to address these environmental inequalities, it is vital that the communities most impacted are at the centre of providing just environmental solutions that don’t further disadvantage them. Thinking beyond the silos of disciplines and creating better nexus between inclusive approaches, equality legislation
and the environment is key to addressing climate injustice and environmental inequalities.
This paper details findings of research underpinned by an innovative interdisciplinary approach undertaken by the authors in 2023. This distinctive approach has provided an
evidence base to develop a novel co-produced Environmental Justice Framework for the public and private sector across a sub-region of the UK. Underpinned by existing theory
and practice around equality impact assessments (within the UK context), environmental justice and co-production principles, the authors present a Framework which encourages
a new interdisciplinary justice centred approach to environmental sustainability decision making. It is argued that this approach (which encourages context based application)could be usefully developed to provide a globally accessible framework for environmental justice
Leadership succession: exploring the recruitment process of 1st Team Managers by Sporting directors in English professional football
Full text linkWhilst leader succession in professional football has received some scholarly attention, we have yet to understand the experiences of senior executives as part of this process. Here, the role of the sporting director is prominent during leader succession, although in this instance remains unexplored. Therefore, this study aimed to investigate the experiences of sporting directors during the hiring process of 1st team managers in senior professional football. In-depth data were collected using semi-structured interviews with sporting directors (n=10), that were subject to a process of thematic analysis. The findings illustrated how potential candidates were identified, the approaches taken to evaluating candidates and how sporting directors skilfully managed and influenced working relationships with others (e.g., chairperson) based upon an understanding of capital theory. The study highlights the potential of capital theory in understanding sporting directors' organisational experiences with empirical and theoretical insights to inform a move towards professionalisation
Interfaces of innovation: exploring technology’s expanding role in events
No full textThe contemporary event landscape is no longer defined solely by physical infrastructure, location, or attendance (Khan et al., 2025; Kumar et al., 2025). Instead, it has evolved into a dynamic, techinfused ecosystem, where digital interfaces and smart technologies enable hybrid, immersive, and highly personalized experiences (Van Winkle et al., 2022). Artificial Intelligence (AI), Virtual Reality (VR), Augmented Reality (AR), and Big Data Analytics are not just adjunct tools; they are becoming central mechanisms through which event competitiveness, audience engagement, and operational efficiency are realized. This special issue, Interfaces of Innovation: Exploring Technology’s Expanding Role in Events, emerges in response to this ongoing paradigm shift. It brings together a curated selection of eight research articles that engage critically and creatively with the evolving interface between technology and events. These contributions reflect an emerging body of work that interrogates the theoretical, practical, and methodological implications of technological advancement in events, drawing attention to the complex intersections between digital innovation, user experience, strategic planning, and broader social and cultural impacts
Assessing hydrogen as an alternative fuel for rail transport – a case study
Full text linkDiesel trains play a vital role in the UK’s rail passenger transport. Despite efforts to expand electrification, over 10% of the UK’s rail routes will remain non-electrified. To reduce emissions and phase out diesel trains by 2040, the UK rail network is actively exploring alternative fuels. This paper presents a comprehensive technical, economic, and environmental analysis of converting diesel trains to hydrogen-powered trains using a hydrogen combustion engine for the first time. A simulation-based methodology has been developed to assess train performance, fuel consumption, and emissions for both hydrogen and diesel engines. The developed methodology has been validated by comparing the predictions against the available experimental data and a very good agreement has been obtained. A case study involving British Class 195 diesel-powered regional trains on the Manchester Airport to Barrow-in-Furness route is analysed. The simulation results show that hydrogen-powered trains achieve zero carbon emissions and exhibit similar NOx emissions to diesel, with a similar performance. Over the train’s 30-year lifespan, green hydrogen can reduce CO2-equivalent emissions by up to 187.4 kt. The study clearly demonstrates that hydrogen combustion engines offer a practical, mid-term solution for decarbonizing regional rail, with much lower conversion costs compared with fuel cell technology
Applied graph data science graph algorithms and platforms, knowledge graphs, neural networks, and applied use cases
No full textApplied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases delineates how graph data science significantly empowers the application of data science. The book discusses the emerging paradigm of graph data science in detail along with its practical research and real-world applications. Readers will be enriched with the knowledge of graph data science, graph analytics, algorithms, databases, platforms, and use cases across a variety of research and topics and applications. This book also presents how graphs are used as a programming language, especially demonstrating how Sleptsov Net Computing can contribute as an entirely graphical concurrent processing language for supercomputers. Graph data science is emerging as an expressive and illustrative data structure for optimally representing a variety of data types and their insightful relationships. These data structures include graph query languages, databases, algorithms, and platforms. From here, powerful analytics methods and machine learning/deep learning (ML/DL) algorithms are quickly evolving to analyze and make sense out of graph data. As a result, ground-breaking use cases across scientific research topics and industry verticals are being developed using graph data representation and manipulation. A wide range of complex business and scientific research requirements are efficiently represented and solved through graph data analysis, and Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Graph Data Science gives readers both the conceptual foundations and technical methods for applying these powerful techniques.
Key features
* Provides comprehensive coverage of the emerging paradigm of graph data science and its real-world applications
* Gives readers practical guidance on how to approach and solve complex data analysis problems using graph data science, with an emphasis on deep analysis techniques including graph neural networks (GNNs), machine learning, algorithms, graph databases, and graph query languages
* Covers extended graph models such as bipartite directed graphs of place-transition nets, graphs with dynamical processes defined on them - Petri and Sleptsov nets, and graphs as programming languages
* Presents all the key tools and techniques as well as the foundations of graph theory, including mathematical concepts, research, and graph analytic