Apprentice nurses’ with specific learning differences: A phenomenological inquiry into their lived experience

Aim To explore the lived experiences of apprentice nurses who have been identified with Specific Learning Differences. Background Apprentice nurses with a learning adjustment plan face unique challenges within their work base, on clinical placements, and in academic settings. Aim To explore the experiences of apprenticeship nurses with specific learning differences. Methods An interpretative phenomenological approach was employed through in-depth, semi-structured interviews with 8 nursing apprentices nursing with a learning plan. Results Four themes discussed are learner identity revealed how participants perceived themselves in relation to nursing, academia and their learning differences; time detailed the apprentices need to engage in extended study time; the changing learning environment describes the impact of physical and social components of the learning space; Technological enhancements/barriers related to modifications made to support learning and the impact they had. Conclusions The findings support collaborative, inclusive teaching and learning practices within the pre-registration apprentice nursing curriculum. Early identification and practical reasonable adjustments in the academic, work base and clinical placement environments can have a positive impact on this group of apprentice nurses

Does Corporate Social Responsibility Influence Customer Loyalty? Insights from the Hotel Industry.

Background This study investigates the relationship between customers' perceptions of corporate social responsibility (CSR), hotel reputation, and customer loyalty within the hospitality sector. This study explored how customers' evaluations of corporate social responsibility (CSR) initiatives influence their loyalty behaviors and whether this relationship is mediated by the hotel's perceived reputation. This study contributes to the literature by integrating corporate social responsibility and hotel reputation into a unified model to predict customer loyalty in the hospitality sector. Methods Data were collected through a structured questionnaire administered via convenience sampling, resulting in 391 valid responses from customers who stayed in star-rated hotels in New Delhi, India. The proposed hypotheses were assessed using PLS-SEM, and the conceptual model was further evaluated for its explanatory and predictive power.ResultsThe study revealed that corporate social responsibility and hotel reputation significantly and positively impact customer loyalty. Furthermore, hotel reputation partially mediates the relationship between corporate social responsibility and customer loyalty. The model demonstrated good explanatory power (R 2 = 0.435 for customer loyalty) and medium predictive relevance (Q 2 > 0.15), supporting the robustness of the proposed structural framework. Conclusions The findings of this study reveal that corporate social responsibility significantly enhances customer loyalty. The partial mediating effect of hotel reputation suggests that while corporate social responsibility independently influences customer loyalty, its impact is further strengthened when accompanied by a strong hotel reputation. This study highlights the strategic importance of aligning corporate social responsibility initiatives with reputation-building efforts to foster deeper emotional and behavioral loyalty among customers

Parent-Carer blame in autism services: A conversation with Alice Running (The Portal Podcast)

In this episode of the Portal Podcast, Professor Sarah Lonbay and Dr Lesley Deacon speak with writer and author Alice Running about the systemic issue of parent-carer blame in autism and SEND (Special Educational Needs and Disabilities) services. Drawing on her lived experience as an autistic mother of neurodivergent children, Alice explains how she has repeatedly encountered damaging narratives from professionals, ranging from assumptions about her parenting to misinterpretations of her children’s needs. Alice discusses her research collaboration with parent advocate Danielle Jata-Hall, which surveyed over 1,000 parent carers across the UK, exposing a widespread culture of blame. She highlights how generic, non-individualised interventions, which are often based on neurotypical benchmarks, fail autistic and PDA (Pathological Demand Avoidance/Pervasive Drive for Autonomy) children, and how inappropriate support can create distress while parents are blamed for “non-compliance.” The conversation explores the biases faced by lone parents and neurodivergent parents, the harmful conflation of disability provision and safeguarding, and the importance of autistic-informed practice, genuine listening, and professional curiosity. Alice also offers practical suggestions for change, including separating safeguarding from provision, adopting a cultural lens to assess autistic families, improving accountability, and increasing professional training

Providing an e-cigarette starter kit for smoking cessation and reduction as adjunct to usual care to smokers with a mental health condition: findings from the ESCAPE feasibility study

Background: Smoking rates in the UK have declined steadily over the past decades, masking considerable inequalities, as little change has been observed among people with a mental health condition. This trial sought to assess the feasibility and acceptability of supplying an electronic cigarette (e-cigarette) starter kit for smoking cessation as an adjunct to usual care for smoking cessation, to smokers with a mental health condition treated in the community, to inform a future effectiveness trial. Methods: This randomised controlled feasibility trial, conducted March-December 2022, compared the intervention (e-cigarette starter kit with a corresponding information leaflet and demonstration with Very Brief Advice) with a ‘usual care’ control at 1-month follow-up. Participants were ≥ 18 years, receiving treatment for any mental health condition in primary or secondary care in three Mental Health Trusts in Yorkshire and one in London, UK. They were also willing to address their smoking through either cessation or reduction of cigarette consumption. The agreed primary outcome measure was feasibility (consent ~ 15% of eligible participants; attrition rate < 30%). Acceptability, validated sustained abstinence and ≥ 50% cigarette consumption reduction at 1-month, were also evaluated and qualitative interviews conducted to further explore acceptability in this population. Results: Feasibility targets were partially met; of 201 eligible participants, 43 (mean age = 45.2, SD = 12.7; 39.5% female) were recruited (21.4%) and randomised (intervention:48.8%, n = 21; control:51.2%, n = 22). Attrition rate was 37.2% at 1-month follow-up and was higher (45.5%) in the control group. At follow-up (n = 27), 93.3% (n = 14) in the intervention group and 25.0% (n = 3) in the control group reported e-cigarette use. The intervention was well received with minimal negative effects. In intention-to-treat analysis, validated sustained abstinence at 1-month was 2/21 (9.5%) and 0/22 (0%) and at least 50% reduction in cigarette consumption 13/21 (61.9%) and 3/22 (13.6%), for the intervention and control group, respectively. Qualitative analysis of participant interviews (N = 5) showed the intervention was broadly acceptable, but they also highlighted areas of improvements for the intervention and trial delivery. Conclusions: Offering an e-cigarette starter kit to smokers with a mental health condition treated in the community was acceptable and largely feasible, with harm reduction outcomes (i.e. switching from cigarette smoking to e-cigarette use and substantial reduction in cigarette consumption) favouring the intervention. The findings of the study will be used to help inform the design of a main trial. Trial Registration: Registry: ISRCTN. Registration number: ISRCTN17691451. Date of registration: 30/09/202

A-407 Harnessing machine learning to the immuohistochemical expression of AMBRA1 and Loricin to identify non-ulcerated AJCC Stage I/II melanomas at high-risk of metastasis

Background: Precision-based personalised biomarkers able to identify both low-risk and high-risk patient subpopulations with localised cutaneous melanoma are urgently needed to guide clinical follow up and treatment stratification. We recently validated the combined immunohistochemical expression of AMBRA1 and Loricrin (AMBLor) in the epidermis overlying non-ulcerated AJCC stage I/II melanomas as prognostic biomarker able to accurately identify genuinely low-risk patient subpopulations (NPV >96%, clinical sensitivity >95%, Ewen et al Brit J Dermatol. 2024). To further identify distinct subsets of patients with non-ulcerated AJCC stage I/II melanomas ar high risk of metastasis, the present study aimed to develop a machine learning (ML) risk-prediction model combining AMBLor ‘at -risk’ status with specific patient clinical and tumour pathological features. Methods: Using commonly and widely used ML models, ML algorithms were trained and tested using three internationally distinct retrospective-prospective cohorts of AMBLor at-risk non-ulcerated AJCC stage I/II melanomas (n=552). Results: Based on a training: test data split of 50:50, 20% of patients were defined as high-risk, with a 5-year recurrence-free survival (RFS) probability of 56% (Log-rank [Mantel-Cox) P < 0.0001, HR 6.88, 95% CI 3[PL1].03-15.63, clinical specificity 87.2%, PPV 44.4%). Further validation of the ML algorithms in a 4th independent retrospective-prospective cohort of 120 AMBLor at-risk non-ulcerated localised melanomas derived from the UK identified 24% patients as high-risk, with a 5-year RFS of 56.3% (Log-rank [Mantel-Cox) P < 0.0001, HR 7.59, 95% CI 2.94-19.6, clinical specificity 82.1%, PPV 50%). Conclusions: Through the proven negative predictive power of AMBLor with the cumulative power of prognostic clinical and pathological features these novel translationally relevant data provide an improved risk- prediction model to stratify patients with non-ulcerated localised melanomas at low or high risk of tumour recurrence thereby aiding optimal personalised patient management and treatment stratification

Developing an AI algorithm to detect predictors of poor performance in a self‐administered, web‐based digital biomarker for Alzheimer’s Disease: proof of concept

Background: The Visual Short Term Memory Binding (VSTMBT) task is a gold‐standard cognitive assessment for the identification of Alzheimer's Disease and associated risk factors, including during the preclinical stage. Previous work from our group (Butler, Watermeyer, …& Parra 2024) demonstrated in a small number (n=37) of healthy older adults that data collected using a web‐based, self‐administrated version of the task provides data comparable to that collected in laboratory conditions. Here we incorporated a machine learning (ML) approach to explore impacts of risk factors on this task in a larger digital dataset. Method: Using data (n=359) collected from an online study incorporating the VSTMBT and lifestyle, psychological, and health data, we created a Binding Cost score which has shown to approximate AD‐related neuropathology (Parra et al., 2024). This categorised participants as either strong‐binders (SB – indicative of no pathology; 85.9% percent of the sample) or weak‐binders (WB – indicative of pathology; 14.1%). We trained three ML algorithms (Random Forest (RF), K‐Nearest Neighbour (KNN) and Decision Tree (DT) by employing SMOTE technique to overcome the imbalance in group distribution. We applied a 10‐fold cross‐validation with hyper‐parameter tuning to optimise the models based on the selected variables (including age, sex, education, BMI, loneliness, and existing‐morbidities) to predict individual’s risk of cognitive impairment based on the groupings (SB vs WB). Models’ performances were examined on 20% of unseen test set. Result: Aside from existing morbidities, which were higher in weak binders (WB = 0.41 (sd+2=0.79); SB =0.22(sd+2=0.49); t=2.21; p=0.03), other measures did not differ between groups. Regarding performance of the ML models, RF achieved the best performance (accuracy: 91%; recall=91%; precision=91%; AUC=97%) compared to KNN (accuracy: 81%; recall=81%; precision=84%; AUC=91%) and DT (accuracy: 81%; recall=81%; precision=82%; AUC= 85%). Feature importance analysis of the RF model suggests mental health, BMI, and fatigue have the highest impact on the prediction model, while sex and multi‐morbidity score have the least impact. Conclusion: The study underscores the potential of web‐based cognitive assessments and ML for remote monitoring and early identification of AD risk factors, contributing to the advancement of accessible tools for early detection

Cleaner air, healthier hospitals: Implementing the UK's Clean Air Hospital Framework

National healthcare services significantly contribute to ambient air pollution and greenhouse gases, particularly through transport and energy generation. Hospitals bring together vulnerable patients in high-traffic settings often in urban areas where there are significant baseline concentrations of ambient pollutants. Therefore, there is a requirement for hospitals to look at ways of reducing their emissions of airborne pollutants, ideally within the framework of achieving net zero goals. This study details the initial implementation of the UK's Clean Air Hospital Framework (CAHF) at two major UK hospitals. CAHF is a proactive self-assessment tool designed to reduce the generation of air pollution from hospital activities. It comprises 215 compliance actions across seven key categories: travel, procurement, design & construction, energy generation, communication & training, outreach & leadership and local air quality. CAHF implementation has focused on sustainable travel options, parking policy, energy efficiency improvements, staff training, education, the adoption of green procurement policies and the incorporation of sustainable travel considerations into new infrastructure designs. Currently, the hospitals are more than half-way towards achieving their implementation goal. To monitor the future overall effectiveness of CAHF, a network of 32 NO2 diffusion tubes was set up across the hospital sites, together with continuous monitors for NO2, PM10 and PM2.5 measurement, and four indoor particulate matter monitors at each hospital. The monitoring programme was supplemented with the development of an ADMS-Urban dispersion model for the site, focussing on emissions from significant adjacent road networks. This study provides an evidence-based exemplar for the CAHF approach and provides a blueprint to support other hospitals to engage in this process

Performing social work: Young fathers’ reflections on social work

Young fathers are marginalized by parenting discourses which focus on women and negative discourses about young people as parents. In this study, young fathers explored their discursive constructions of their own and social workers’ identities and considered their perceptions of social workers as professionals involved in their children’s lives, as well as their thoughts about how they felt social workers view their role as fathers. The study applied Butler’s performativity and gender performances with young fathers to explore how they think social workers perform social work and used critical discourse analysis to examine data from an online focus group of young fathers. While the fathers demonstrated capacity to recognize their own parenting and how this has evolved, they explained social workers expect them to reproduce negative parenting stereotypes and inhabit a role less deserving of support than mothers. This study highlights how young dads experience intersectional discrimination as young people and fathers and concludes by recommending that safe spaces are needed for relationships of trust to be developed between social workers and young dads where their own needs for support can be voiced. Meeting these needs is critical if fathers are to be encouraged and recognized as involved parents

From "Mirror Flower, Water Moon" to Multi-task Visual Prospective Representation Learning for UAV Indoor Mapless Navigation

Vision-based deep learning models have been widely adopted in autonomous agents, such as Unmanned Aerial Vehicles (UAVs), particularly in reactive control policies that serve as a key component of navigation systems. These policies enable agents to respond instantaneously to dynamic environments without relying on pre-existing maps. However, there remain open challenges to improve the agent’s reactive control performance: (1) Is it possible and how to anticipate future states at the current moment to benefit control precision? (2) Is it possible and how to anticipate future states for different sub-tasks when the agent’s control consists of both discrete classification and continuous regression commands? Inspired by the Chinese idiom "Mirror Flower, Water Moon", this paper hypothesizes that future states in the latent space can be learnt from sequential images using contrastive learning, and consequently proposes a light-weight Multi-task Visual Prospective Representation Learning (MulVPRL) framework for benefiting reactive control. Specifically, (1) This paper leverages the advantage of contrastive learning to correlate the representations obtained from the latest sequential images, and one image in the future. (2) This paper constructs an integrated loss function of contrastive learning for classification and regression sub-tasks. The MulVPRL framework outperforms the benchmark models on the public HDIN and DroNet datasets, and obtained the best performance in real-world experiments (46.9????, 177???? ????????. SOTA 27.3????, 136????). Therefore, the multi-task contrastive learning of the light-weight MulVPRL framework enhances reactive control performance on a 2D plane, and demonstrates the potential to be integrated with various intelligent strategies, and implemented on ground vehicles. Keywords: UAV, Indoor Unknown Environment, Mapless Navigation, Contrastive Learning, Visual Prospective Representation Learning (VPRL), Prospective Regression-aware Representation, Prospective Classification-aware Representatio

Neurodivergent Education and Lifelong Learning

Description: Neurodivergent education and lifelong learning represent a transformative approach to understanding and supporting individual thinking, learning, and engagement. As neurodiversity awareness increases, traditional educational models fail to meet the needs of all learners. Embracing neurodivergent perspectives calls for inclusive, flexible, and personalized educational strategies that extend beyond childhood into adulthood. Lifelong learning becomes a tool for personal and professional development, and a critical path for neurodivergent individuals to thrive, contribute to society, and pursue personal fulfillment. Neurodivergent Education and Lifelong Learning explores the application of inclusive education practices for accessible learning. It examines the lived experiences of neurodivergent individuals to foster a humanized approach to intersectionality and advocacy in educational contexts. This book covers topics such as mental health, childhood development, and higher education, and is a useful resource for educators, sociologists, academicians, researchers, and scientists. Coverage: The many academic areas covered in this publication include, but are not limited to: Childhood Development Digital Technology Diversity, Equity, and Inclusion Higher Education Inclusive Education Intersectionality Learning Disabilities Lifelong Learning Mental Health Neurodivergence Student Experiences Trauma-Informed Educatio