Telemedicine use in rural areas of the United Kingdom to improve access to healthcare facilities: A review of current evidence

Background Rural populations in the UK face healthcare inequities despite the NHS's aim of providing universal healthcare. These disparities include restricted access, transportation challenges, and healthcare workforce shortages, resulting in delayed care and poorer health outcomes. This research aims to investigate the use of telemedicine in rural areas of the United Kingdom to improve access to healthcare facilities. Methods The research process combines a systematic literature review with a thematic analysis using open coding. The results were presented through thematic representation from an open-coding method, following an established search strategy, inclusion/exclusion criteria, a two-step screening procedure, and data extraction. The PRISMA framework was used to screen the articles for the research. Results Findings reveal that telemedicine significantly improves access to healthcare in rural areas by reducing travel barriers, enhancing mental health services, and increasing patient engagement. Studies highlight its expanding use during pandemics, cross-border reach, and beneficial effects on mental health services. Digital literacy programs and targeted resource distribution were identified as critical to maximizing the effectiveness of telemedicine. Measures like digital literacy and equitable resource allocation are called for in response to issues like specialized care delivery and equitable access. Together, these projects present a thorough strategy for using telemedicine's promise of equal access to healthcare in rural areas. Conclusion Even though studies show that telemedicine was used more frequently during the epidemic, the review underscores the need for enhanced digital literacy and infrastructure to ensure equitable access. Difficulties, including legal complications, a lack of technological literacy, and communication obstacles, still exist. Initiatives to promote digital literacy, fair resource distribution, and regulatory changes for smooth integration are highlighted in the suggested solutions. Overall, telemedicine holds the potential to significantly reduce healthcare disparities in rural areas, provided these challenges are addressed

The Social Prescribing Link Worker—Clarifying the Role to Harness Potential: A Scoping Review

Recent work outlines definitions of social prescribing, but the role of a social prescribing link worker (SPLW) remains ill defined. Core components of the role must be clarified to enable the study of its impact in connecting people to community-based support and subsequent outcomes. This review compiles and summarises published information on the SPLW role. A scoping review was completed using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis for Scoping Review. Suitable online databases were searched using identified terms, a review of the reference lists of identified papers was completed and relevant grey literature was identified through Google Scholar. Relevant reports from UK-based social prescribing networks and government organisations were gathered. Eligibility of each paper was determined based on the specified criteria. Inclusion criteria were identified using the PCC (Population; Concept; Context) framework. Of the 251 search results originally identified, 15 peer-reviewed papers met the criteria for inclusion. Five additional published reports from government and community organisations and networks were identified. Data were extracted and collated in tabular form. Thematic data analysis highlighted four common themes clarifying the role of the SPLW and identifying improvements required to advance social prescribing referral processes. (1) SPLW works in collaboration with the participant, to identify personal needs and goals, and monitors progress over time. (2) SPLW connects service users to community/statutory support. (3) SPLW views health in a holistic manner. (4) The importance of training for SPLWs, and those referring into the system, to improve the referral process. Disparity in language and roles is evident, making it difficult to describe and compare the role across social prescribing services. The importance of training is outlined, and training recommendations are made. Additional effort is needed to clarify the role, impact and training requirements within social prescribing, to strengthen the evidence base, and allow applicability and transferability across services

Carbon Capture Efficiency of Ultrafine Cementitious Substituents and Fine Aggregate Alternatives Subjected to Accelerated CO2 Curing

This manuscript examines the quantification of CO2 uptake, calcium hydroxide (Ca(OH)2, CH) and calcium carbonate (CaCO3, CC) formed for processed recycled concrete fines (RCF), supplementary cementitious materials (SCMs) and various sustainable fine aggregate alternatives subjected to accelerated carbonation process. A thermogravimetric (TG) analyser was used to enumerate the mass loss consequential from these compounds' breakdown at particular temperature range (400-500 ◦C for CH, 600-800 ◦C for CC, and CO2). The increased areas of peaks from fourier transform infrared spectroscopy (FTIR) analysis confirmed the presence of calcite and vaterite polymorphs for carbonated RCF and SCMs at 875 cm-1 and 714 cm-1 respectively whereas the formation of calcium silicate hydrate (Ca2.25[Si3O7.5(OH)1.5].8H2O or CSH gel) is confirmed by the increased stretching vibrations of Si-O bond at 970 and 1030 cm-1. The X-ray diffraction (XRD) found the presence of useful compounds such as aragonite, calcium silicate hydroxide (Ca4Si5O13.5(OH)2) and portlandite that further confirmed the carbonation of RCF, SCMs and various fine aggregate alternatives. The formation of these compounds in carbonated specimens resulted in a significant fall in Ca/Si atomic ratio to a maximum of 98% that further signifies the denseness in microstructure owing to precipitation of CaCO3 and CSH gel deposition. The filled cracks and pores represented by scanning electron microscopy (SEM) images in carbonated specimens demonstrates the suitability of adopted carbonation regimes. The physical performance of RCF, SCMs and various fine aggregate specimens post accelerated carbonation highlights the increase in bulk density, specific gravity and reduced water absorption levels and volume changes that is an area of grave concern for incorporating recycled materials in construction sector. In addition, the CO2 uptake of various carbonated specimens is found using TG analysis demonstrates the highest uptake for RCF at 32.4% surpassing various other utilised SCMs and fine aggregate alternatives used in the research work. It is to be noted that metakaolin and ultrafine fly ash shows minimal CO2 uptake owing to the manufacturing process. The findings of this study recommend the use of processed RCF and various other SCMs and fine aggregate alternatives for potential carbon dioxide sequestration through accelerated carbonation technology

How democratically elected mayors can achieve mission-oriented policies in turbulent times

The aim of this article is to explore how democratically elected mayors can achieve mission-oriented policies in turbulent times. Drawing on 132 interviews with decision makers in England, this article uses the case of healthy urban development to explore the role of elected mayors in mission delivery. Findings show that mayors can be figureheads for a place, work directly towards national missions, implement cross-cutting programmes, convene partnerships and lead local innovations with new evidence and data. However, more central government support is needed with investment in capacity, a broader range of powers and greater freedom from central targets and siloes

How can research support volunteering?

Over the past 10 years, I have taught students fresh from work or voluntary experience in the charity and non-profit sectors. Students frequently share their experiences of surprise and anxiety as the volunteered. These have included a consciousness of exclusionary management practices towards people from the global majority, shock at the harsh realities for migrants at the US-Mexico border and consternation at the lack of recycling practices in a community setting

AI-Assisted Physiotherapy for Patients with Non-Specific Low Back Pain: A Systematic Review and Meta-Analysis

Background: Non-specific low back pain (LBP) is a widespread condition with significant impacts on physical activity, muscle strength, psychological well-being, and economic status. Traditional physiotherapy shows variable efficacy, prompting growing interest in AI-assisted physiotherapy for its potential to offer personalized feedback and multidisciplinary care integration. Objective: This systematic review and meta-analysis aimed to evaluate AI-assisted physiotherapy’s effectiveness in reducing pain intensity and functional impairment and improving mental health compared to usual physiotherapy. Method: A comprehensive search strategy was employed across Embase, MEDLINE, Cochrane Library, and Web of Science databases from inception to 30 May 2024. Comparative studies were identified and screened using PICOS criteria. Data extraction involved detailed study characteristics and outcomes, with methodological quality assessed via the Cochrane Risk of Bias tool. Meta-analyses using random-effects models calculated standardized mean differences (SMDs). Results: Eight studies met the inclusion criteria. Compared to usual physiotherapy, AI-assisted physiotherapy did not demonstrate any statistically significant differences in outcomes across the aspects studied, including pain intensity (SMD = −0.2711, 95% CI: −0.5109 to −0.0313, p = 0.267), functional impairment (SMD = −0.2508, 95% CI: −0.5574 to 0.0559, p = 0.1089), and mental health (SMD = −0.0328, 95% CI: −0.1972 to 0.1316, p = 0.6956). These findings indicate that AI-assisted physiotherapy had no demonstrable additional effect compared to usual physiotherapy for patients with LBP. Sensitivity analyses were conducted to address inter-study heterogeneity, confirming the robustness of these results. Conclusions: While AI-assisted physiotherapy shows potential in managing LBP by providing personalized treatment and feedback, the current evidence does not demonstrate significant advantages over usual physiotherapy. Further large-scale, long-term, and methodologically rigorous randomized controlled trials are necessary to validate these findings, assess their clinical relevance, and explore broader public health applications

Enhanced network synchronization connectivity following transcranial direct current stimulation (tDCS) in bipolar depression: Effects on EEG oscillations and deep learning-based predictors of clinical remission

Aim To investigate oscillatory networks in bipolar depression, effects of a home-based tDCS treatment protocol, and potential predictors of clinical response. Methods 20 participants (14 women) with bipolar disorder, mean age 50.75 ± 10.46 years, in a depressive episode of severe severity (mean Montgomery-Åsberg Rating Scale (MADRS) score 24.60 ± 2.87) received home-based transcranial direct current stimulation (tDCS) treatment for 6 weeks. Clinical remission defined as MADRS score < 10. Resting-state EEG data were acquired at baseline, prior to the start of treatment, and at the end of treatment, using a portable 4-channel EEG device (electrode positions: AF7, AF8, TP9, TP10). EEG band power was extracted for each electrode and phase locking value (PLV) was computed as a functional connectivity measure of phase synchronization. Deep learning was applied to pre-treatment PLV features to examine potential predictors of clinical remission. Results Following treatment, 11 participants (9 women) attained clinical remission. A significant positive correlation was observed with improvements in depressive symptoms and delta band PLV in frontal and temporoparietal regional channel pairs. An interaction effect in network synchronization was observed in beta band PLV in temporoparietal regions, in which participants who attained clinical remission showed increased synchronization following tDCS treatment, which was decreased in participants who did not achieve clinical remission. Main effects of clinical remission status were observed in several PLV bands: clinical remission following tDCS treatment was associated with increased PLV in frontal and temporal regions and in several frequency bands, including delta, theta, alpha and beta, as compared to participants who did not achieve clinical remission. The highest deep learning prediction accuracy 69.45 % (sensitivity 71.68 %, specificity 66.72 %) was obtained from PLV features combined from theta, beta, and gamma bands. Conclusions tDCS treatment enhances network synchronization, potentially increasing inhibitory control, which underscores improvement in depressive symptoms. Baseline EEG-based measures might aid predicting clinical response

How culture and legal environment affect classification shifting? Global evidence

This study examines the interplay between various cultural characteristics and the legal environment on classification shifting using a global sample that enables variability in underlying cultural characteristics across countries while controlling for heterogeneity. Given that both culture and the legal environment tend to exhibit low variability over time, our international cross-country analysis with diverse cultural dimensions and legal frameworks enhances the robustness of our empirical findings. Our identification strategy employs several models and shows the significant impact of culture on classification shifting and the interactions between national culture and the legal environment on classification shifting behaviour, though there is variability across countries. We also find that certain traits of culture induce classification shifting. We highlight that strengthening the legal environment becomes crucial in creating an institutional framework that effectively curbs unethical practices induced by certain national culture traits and enhances transparency and accountability in financial reporting

Innovative computation to detect stress in working people based on mode of commute

Introduction: Commuting is an integral part of modern life for many people, shaping daily routines and impacting overall well-being. With various transportation options, including driving, public transport, walking, and cycling, commuters encounter various experiences and challenges in their everyday journeys. Understanding how different modes of commuting affect stress levels is essential for improving public health and informing transportation planning. This study develops advanced machine-learning techniques to explore the connection between commuting methods and stress levels. Methods: This research examines how different c ommuting m odes a ffect st ress le vels us ing machine learning methods. The study analyses data collected from 45 individuals who regularly commute to work, focusing on driving, walking, cycling, and public transport modes. Non-invasive wearable sensors were utilised to gather electroencephalography (EEG), blood pressure (BP), and heart rate (HR) data for five consecutive days for each participant. Additionally, qualitative data was collected using the Positive and Negative Affect Schedule (PANAS) questionnaire to assess participants’ emotional responses before and after their commute. The research focuses on developing a machine learning-based model to predict the commute’s impact and monitor the stress level due to the commute mode. In research, objective and subjective factors shape the research process and outcomes. Understanding the interaction between these factors is essential for conducting thorough and reliable research that produces valid results. Our study utilises datasets incorporating qualitative and quantitative data from questionnaires and human biosignals. Results: Similarly, this research developed various machine learning algorithms to detect stress levels based on commuting mode. The results indicate that the Linear Discriminant Analysis technique achieved an accuracy of 88%, while Logistic Regression reached 90.66% accuracy. The Boosted Tree algorithm produced the best performance, with an accuracy of 91.11%. Furthermore, incorporating personalized parameters into the data improved the accuracy of these algorithms in detecting stress levels. Cross-validation was also utilized to mitigate the risk of overfitting, ensuring robust and reliable model performance. Conclusions: The findings reveal that human bio-signals tend to increase following commuting, irrespective of the mode, with driving identified a s t he most s tressful o ption. C ommuters u sing passive modes of transport experience elevated stress levels compared to those using active modes. This research underscores the importance of understanding the connection between commuting modes and stress, providing key insights into the potential health impacts of daily travel. The development of an intelligent model to predict stress levels based on commuting mode offers valuable contributions to public health and transportation planning, with the goal of enhancing well-being and improving commuters’ quality of life