4101 research outputs found
Sort by
Performance against quality indicators in the initial assessment of patients with respiratory infections in acute medicine services
No full textIntroduction Hospital attendances due to respiratory infection peak in winter, contributing to pressures within acute services. We assessed the prevalence of suspected respiratory infection within acute medical admissions during winter and evaluated performance against recommendations for initial assessment. Methods Data were collected through the Society for Acute Medicine (SAM) Benchmarking Audit, comprising a hospital-level survey and 24-hour patient-level data collection for unplanned acute medical attendances on 22 February 2024. Performance metrics assessed included those from the SAM's clinical quality indicators (CQI) for medical admissions, and British Thoracic Society (BTS) guidelines for community acquired pneumonia. Results Data were available for 4390 patients at 76 hospitals. Suspected respiratory infections accounted for 22.8% of all unplanned medical attendances; these patients were older (age ≥70 years: 58.2% vs 44.7%, p<0.001) and had higher National Early Warning Score 2 (NEWS2) scores (NEWS2 ≥3: 63.8% vs 23.8%, p<0.001) than those without respiratory infection; they were more likely to be assessed in the emergency department (80.8% vs 63.7%, p<0.001), and had lower rates of discharge without overnight admission (14.9% vs 35.9%, p<0.001). 71.0% of patients underwent a chest X-ray within 4 hours of arrival; 27.0% were reported within 12 hours. Antibiotics were administered ≥4 hours from arrival in 32.9%. Performance against these indicators varied between hospitals. Nine hospitals (12.7%) had a separate respiratory admission service; this was not associated with improved performance against SAM CQIs or BTS guidance. Conclusion Respiratory infections contribute significantly to acute medical attendances via the emergency department. There remains significant scope to improve key steps in initial assessment and management
Laughing in the face of adversity: stand-up comedy as a tool for improving mental health and well-being
No full textPurposeThis paper aims to explore how stand-up comedy can be used as an applied community-based intervention to promote mental health and social well-being.Design/methodology/approachDrawing on the author’s dual role as a stand-up comic and academic, the paper reflects on practice-led workshops and lived experience to examine the therapeutic potential of performing comedy.FindingsStand-up comedy offers a powerful medium for reframing traumatic experiences, fostering social connectedness and enhancing emotional resilience and enables performers, including those with neurodiverse traits, to reclaim personal narratives and engage in peer-supported communities of practice.Practical implicationsPractitioners and policymakers should recognise the value of participatory arts, particularly comedy-based, in the recovery and resilience-oriented mental health frameworks.Originality/valueThis paper contributes a unique perspective by framing stand-up comedy as both a creative and therapeutic tool for mental health intervention and advocacy
The Development of therapeutic coaching and its impact upon fatigue related conditions Critical Commentary
No full textThis critical commentary outlines my contributions to knowledge over the last 23 years of my career in developing the methodology of Therapeutic Coaching (TC), and its application to fatigue related conditions such as ME/CFS, Fibromyalgia, Lyme Disease and Long-COVID. Using the methodology of autoethnography, I have acted as both the researcher and participant to explore the landscape within which I developed this framework, and the key factors in bringing this work to an international audience of millions of people. I begin with a selective literature review of the historical and current mainstream treatments for fatigue related conditions, with a particular emphasis on how these have been negatively received by patient communities. I go on to outline the history behind the development of my clinic, the Optimum Health Clinic, and how it found significant international success in creating a framework which addresses some of these limitations. I then outline how I have used various online learning mediums to reach a wider audience, in particular my Super Conference series which has been attended by over two million people. This is followed by a chapter introducing my professional training programs, which have trained hundreds of clinicians in over 40 countries. I conclude with reflections upon the key learnings of my career to date, and aspirations for the future, in particular a return to my academic roots, which I hope this PhD will help facilitate
Evaluation and Enhancement of Automatic License Pattern Recognition System
No full textVehicle cloning, the act of duplicating licence plates and in some cases whole car is an emerging threat which presents a challenge that has not been catered for in existing solutions. Although the Automatic Licence plate Recognition (ALPR) system has been at a mature stage for a while, this unique problem is not addressed, and we have not come across any commercial solution either. This paper is first to address two key areas in enhancement of ALPR system. First the evaluation metric was developed to address unfairness of accuracy of string level match enhancing accuracy from 43% to 96%. Secondly, we introduced a novel framework using 'Usual Route Identification' algorithm to analyse a Vehicle's usual journey which helps to flag if unusual routes are taken or located outside off a set radius indicating a potential case of vehicle cloning. This study will show how appropriate metric can enhance the capability of ALPR systems, proposes a functionality that aids law enforcement authorities, and adds to academic knowledgebase
Designing feedback literacy activities with generative AI: supporting students to receive, internalise, and act on feedback
No full textGraphene oxide–based wireless sensor in fibre–reinforced hybrid composites for incipient fire detection
No full textFire sensors offer an effective strategy for mitigating fire hazards. This work reports the development of self-sensing glass fibre–reinforced composites (GFRCs) by incorporating a graphene oxide (GO)–aramid sensing layer into their structure. Two sensor configurations were explored: GO–coated aramid webs (GO–AW) and GO–aramid nanofibre films (GO–ANF). These sensors function via the thermal reduction of GO to conductive reduced graphene oxide (rGO) under heat or flame, enabling rapid (<1 s) fire detection and real-time wireless alerts via an IoT–enabled system. The GO–AW web strip, patterned with conductive ink electrodes and embedded in a GFRC laminate, effectively responded to both conductive (direct contact) and radiative (external heat flux) heat, acting as a robust pre–fire sensing material. Integration with an ESP32 microcontroller enabled wireless, real–time monitoring and instant alerts, ensuring practical applicability in fire–safety–critical environments. Furthermore, the incorporation of GO–AW enhanced the thermal and mechanical properties of the composite, with the flexural modulus increasing from 3.1 to 6.5 GPa and the glass transition temperature from 86 °C to 94 °C. The presence of GO–AW in the GFRC also reduced flammability of the composite, indicated by reduction in the peak and total heat release rate by 22 and 45 %, respectively in cone calorimetric experiments. Overall, the integration of GO–AW not only imparted fire-sensing functionality but also improved the composite's structural integrity and flame retardancy, demonstrating broad potential for structural and industrial applications
Fire retarded fibre-reinforced composites: Effect of heat and fire on carbon fibre oxidation and resultant properties
No full textThis study has investigated the impact of fire retardants in carbon fibre-reinforced epoxy composites (CFRC) on physico-mechanical and oxidative properties of carbon fibres after exposure of CFRCs to high temperatures and fire. Three fire retardants were chosen based on their activity in condensed phase (ammonium polyphosphate, APP) and/or vapour phase (9,10-dihydro-9-oxa-10-phosphaphenanthrene-10-oxide, DOPO, and resorcinol bis-(diphenyl phosphate), RDP). The composites were subjected to high heat fluxes (75–116 kWm-2) and fire using a cone calorimeter and propane burner. Post-exposure, the carbon fibres extracted from different plies were analysed for surface oxidation, mass loss, diameter reduction, and changes in tensile and electrical properties. Carbon fibres exhibited differing degrees of oxidation across the plies, with surface ply fibres showing greater oxidation and diameter reductions, while underlying plies experienced limited oxidation due to restricted oxygen access. The charred residues from fire-retarded samples (residue levels: APP > RDP > DOPO > control) adhered to the fibres, reducing oxidation and preserving tensile properties. However, the charred residues increased the electrical conductivity of the carbon fibres. This analysis has enabled the evaluation of each retardant's effectiveness
A Critical Postmodern Feminist Exploration of Identity, Agency and Academic Drivers to Study and Secure Graduate Employment Non-Traditional British South Asian Women Studying for a Business and Management Undergraduate Degree
No full textThe relationship between Higher Education (HE), patriarchal cultural structures, and career advancement for non-traditional British South Asian women (NT-BSAW) remains unexplored, despite growing disparities in graduate outcomes. This study critically examines how UK Government HE policies, Higher Education Institution (HEI) practices, and community expectations shape or constrain the educational and career trajectories of NT-BSAW. It brings needed attention to the specific challenges faced by ethnically diverse mature female students, often hidden within broader policy categories.Using a multi-method qualitative approach, the research draws on semi-structured interviews with NT-BSAW (n=7) and an employer-led focus group (n=6) to explore barriers in the transition from education to employment. Grounded in postmodern feminist theory, intersectionality, and agentic perspectives, this study develops a three-layered model designed to enhance career support beyond traditional roles. The HEI ABC Career Framework integrates lived experiences, cultural influences, and targeted interventions to address inequalities beyond tokenistic, short-term initiatives. A key contribution is the introduction of delayed agency and reverted agency, extending bounded agency theory. Reverted agency explains how NT-BSAW navigate constrained, postponed, or reversed decisions under intersecting cultural, structural, and institutional pressures, before, during, and after studies. These insights deepen understanding of how social constraints impact motivation, choice, and outcomes. The findings reveal that, while HE is promoted as a driver of social mobility, systemic disparities persist. Employer insights expose recruitment barriers, reinforcing weak commitments to inclusive hiring and career advancement. By advocating for a structured, intersectional approach, this research provides a timely, practice-driven response to the long-standing gaps in UK higher education (HE) policies and practices
An examination of Young Onset Dementia and social inclusion relating to personal experiences and the impact on the individual, their family and external network
No full textThis study explores the measurable barriers hindering social inclusion, for individuals living with youngonset dementia (YOD) and their families. Drawing on social constructionism and feminist perspectives,qualitative methods including semi-structured interviews, focus groups, and a case study wereemployed to delve into subjective experiences and perceptions.Narratives from nine individuals living with YOD and five family members, all engaged in support groupswithin North West England, shed light on the explored barriers to social inclusion. The study examinesthe impact of role, occupation, self-identity, and altered social interactions, alongside the recognitionof YOD as a disability.Using Braun and Clarke’s thematic analysis (2006), three prominent themes emerged from thefindings: i) acknowledging a diagnosis of YOD ii) adapting to change and iii) resilience. These themesintersect to emphasise the necessity of a deeper comprehension of signs and symptoms of dementiain younger individuals, timely diagnosis irrespective of age, and recognition of the distinct challengesfaced by younger individuals living with dementia.To facilitate change, the Circle of Empowerment model is proposed, offering recommendations forpolicy and practice adjustments. These include reframing YOD as a disability and recognising thatindividuals living with YOD can still learn, engage socially, and find purpose and value in society.Embracing the Circle of Empowerment model in practice, alongside acknowledging the benefits ofspecialised support within YOD-specific communities, holds the promise of fostering a more positiveexperience for individuals living with YOD, wherein individuals feel valued and integrated into society
Automated Infrastructure Sustainability Assessment: A Deep Learning Approach For Real-Time CO2 Image Analysis
No full textThis study investigates the potential of using deep learning for real-time image analysis in assessing sustainable infrastructure and urban development. Convolutional Neural Networks (CNNs) are implemented to evaluate live-captured building images, enabling automated classification and data extraction for decision-making. The proposed approach overcomes the limitations of existing methods by facilitating real-time analysis and large-scale data processing. A dataset exceeding 12,000 images rigorously evaluates the CNN model's performance. This research establishes a framework for leveraging deep learning for real-time assessment of sustainable infrastructure, paving the way for improved data-driven urban planning and development decision-making. The study confirms that the Inception Net V3-based feature extraction technique accurately classifies images based on CO2 emission levels. This classification task is best performed using the Neural Network model. Advanced feature extraction techniques are essential for improved environmental image classification