The changing shape of English general practice: a retrospective longitudinal study using national datasets describing trends in organisational structure, workforce and recorded appointments.

OBJECTIVE: To describe trends in the organisational structure, workforce and recorded appointments by role in English general practice. DESIGN: Retrospective longitudinal study. SETTING: English general practice. DATA SOURCES AND PARTICIPANTS: NHS England, Office for Health Improvement and Disparities and Care Quality Commission national administrative datasets covering between 5 to 10 years from 2013 to 2023. RESULTS: Between 2013 and 2023, the number of general practices fell by 20% from 8044 to 6419; the average practice list size increase by 40% from 6967 to 9724 patients. The total population covered by providers with over 100 000 registered patients reached 2.3 million in 2023 compared to 0.5 million in 2017. The proportion of practices under individual ownership decreased from 13% to 11% between 2018 and 2023; there was little change in the proportion owned by partnerships, incorporated companies or NHS bodies, which respectively averaged around 80.3%, 6.9% and 0.7%. Between 2015 and 2022, there was a 20% rise in the total full-time equivalent (FTE) general practice workforce, including Primary Care Network staff, from 1.97 to 2.37 per 1000 patients because of an increase in multidisciplinary other 'Direct Patient Care' (DPC) and administrative roles. The number of nurses remained stable, and the number of qualified general practitioners (GPs) decreased by 15%. In September 2022, there were 0.45 FTE qualified GPs per 1000 patients; GPs and other DPC roles, excluding nurses, each represented 19% of the FTE per 1000 patients workforce; administrative roles represented 51%. The general practice workforce is predominantly female. A quarter of GPs qualified overseas. Between 2018 and 2023, there was no clear upward or downward trend in total appointments per 1000 patients with, on average, half provided by GPs. CONCLUSIONS: Since 2013, there has been a shift in general practice towards larger practices with more multidisciplinary teams, alongside a reduction in the number of FTE qualified GPs per 1000 patients. We recommend that the impacts of these changes on access, quality and costs are closely monitored.

Cultivating sustainability: a multi-assessment of groundwater quality and irrigation suitability in the arid agricultural district of Dzira (Ksour Mountains, Algeria).

Groundwater serves a range of essential functions such as supplying drinking water, facilitating agricultural practices, and supporting industrial processes. This study examines with multiple methods the quality of groundwater in the agricultural region of Dzira, Algeria. By collecting 38 groundwater samples of different wells and boreholes, valuable awareness of the aptness of groundwater for irrigation in this arid landscape was gained. Most wells met Food and Agriculture Organization (FAO) criteria for the total dissolved solids (TDS) and the potential of hydrogen pH, but some areas had higher mineral content and electrical conductivity. Results show significant TDS variations, with 10.81% of wells exceeding limits and acceptable pH levels. Elevated EC values in 67.57% of wells show high salinity, affecting soil and plant growth. Major ions such as Mg2+ and SO4- exceeded FAO standards in 43.24% and 64.86% of wells, respectively, highlighting substantial mineral content in the groundwater. Suitability indices reveal that most wells pose low sodium hazards and are generally suitable for irrigation, though some areas face moderate to high restrictions. The irrigation water quality index (IWQI) ranged from 45.36 to 96.30, averaging 80.77, with 54.04% classified as "low restriction," suitable for sandy soils with good permeability but requiring caution on salt-sensitive soils. Hydrogeochemical analysis using principal component analysis (PCA) and hierarchical cluster analysis (HCA) identifies rapid evaporite dissolution from Triassic saline formations, with a correlation matrix showing associations between TDS and Ca2⁺, Mg2⁺, Na⁺, Cl⁻, and SO42⁻. This mineralization is likely from gypsum and halite. Zoning maps based on IWQI and other parameters depicted spatial variations in groundwater quality, guiding effective irrigation management strategies. Overall, the study underscores the importance of comprehensive water quality assessment for sustainable agriculture and emphasizes the need for targeted interventions to mitigate potential challenges associated with soil salinity and sodicity. Therefore, these findings can be useful to decision-makers and stakeholders in order to optimize water use and protect this vital resource.

Potential impacts of climate change on cephalopods in a highly productive region (Northwest Pacific): Habitat suitability and management.

Cephalopods occupy a mid-trophic level in marine ecosystems and are vital both ecologically and as fishery resources. However, under the pressure of climate change and fishing, the sustainability of cephalopod resources requires reasonable management. This study aims to study climate change and fishing impacts on the common economic cephalopod species habitats using species distribution models. We take the northwest Pacific Ocean region as an example, which stands out as a significant region for cephalopod production around the world. Results found that the habitats of cephalopods are moving to higher latitudes or deeper waters (Bohai Sea, mid-bottom Yellow Sea, or the Okinawa Trough waters) under climate change. Additionally, these regions are currently under lower fishing pressure, which suggests that species migration might mitigate the effects of warming and fishing. This study provides the large-scale assessment of the distribution range of cephalopods affected by climate change coping with fishing pressure in the northwest Pacific Ocean. By identifying climate refuges and key fishing grounds, we underscore the importance of this information for managing cephalopod resources in the context of climate adaptation and sustainable fishing practices.

Integrated environmental management and GPS-X modelling for current and future sustainable wastewater treatment: A case study from the Middle East.

In the context of today's rapidly changing environmental challenges, accurately predicting the performance and efficiency of environmental management strategies is crucial. Particularly in the Middle East, where research on wastewater treatment plants (WWTPs) is notably lacking, addressing this need is imperative. This study investigates the treatment efficiency of a wastewater treatment plant and proposes various techniques to enhance its performance. Employing a case study method, we utilise the GPS-X model to forecast the plant's performance under diverse scenarios, offering solutions for future challenges. The results reveal that the current plant layout operates efficiently, with removal efficiencies for Total Suspended Solids (TSS), Chemical Oxygen Demand (COD), and Biochemical Oxygen Demand (BOD) at 98.3 %, 95.1 %, and 96.1 %, respectively. The outlet Dissolved Oxygen (DO) of 1.9 mg/L meets local wastewater reuse standards. Furthermore, the GPS-X model forecasts the plant's performance under different scenarios, suggesting the feasibility of a new layout within 20-25 years and the need for additional units after 40 years. As inflow approaches maximum design capacity, simulation results underscore the importance of utilising the full plant design and expanding it for optimal operation over 60 years. This research provides critical insights for improving WWTP performance and emphasizes the significance of strategic planning in addressing long-term environmental management challenges. Moreover, this study represents a pioneering effort in addressing critical water scarcity challenges in Jordan by exploring the potential of treated wastewater (TWW) as a sustainable solution, thus contributing to the advancement of environmental management practices in the region.

Leveraging Edge Computing for Video Data Streaming in UAV-Based Emergency Response Systems.

The rapid advancement of technology has greatly expanded the capabilities of unmanned aerial vehicles (UAVs) in wireless communication and edge computing domains. The primary objective of UAVs is the seamless transfer of video data streams to emergency responders. However, live video data streaming is inherently latency dependent, wherein the value of the video frames diminishes with any delay in the stream. This becomes particularly critical during emergencies, where live video streaming provides vital information about the current conditions. Edge computing seeks to address this latency issue in live video streaming by bringing computing resources closer to users. Nonetheless, the mobile nature of UAVs necessitates additional trajectory supervision alongside the management of computation and networking resources. Consequently, efficient system optimization is required to maximize the overall effectiveness of the collaborative system with limited UAV resources. This study explores a scenario where multiple UAVs collaborate with end users and edge servers to establish an emergency response system. The proposed idea takes a comprehensive approach by considering the entire emergency response system from the incident site to video distribution at the user level. It includes an adaptive resource management strategy, leveraging deep reinforcement learning by simultaneously addressing video streaming latency, UAV and user mobility factors, and varied bandwidth resources.

Identifying resource-conscious and low-carbon agricultural development pathways through land use modelling.

Increasing agricultural production with current resources and technology may lead to increased GHG emissions. Additionally, large population countries like India face substantial challenges in terms of food demand, agro-ecological heterogeneity, carbon footprint and depleting natural resources, thus increasing the decision complexities for policymakers and planners. We aim to examine the potential of producing more food from available agricultural land with low-carbon (reduced GHG emissions) and resource-conscious (optimal resource use) options. The current study develops multiple calorie production and emission-centric land use using a land use optimization model wherein the calorie production and emission objective, resource and emissions constraints, and food production targets interact across multiple spatial levels. The capabilities of the developed model are demonstrated with a case study in India targeting ten crops (grown over two seasons) covering three food groups (cereals, legumes, and oilseeds). Three hypothetical scenarios for each objective of maximizing calories production (Calories-nation, Calories-group, Calories-crop) and minimizing GHG emissions (Emissions-nation, Emissions-group, Emissions-crop) are developed concerning targets of national crop production (Calories-nation, Emissions-nation), state food groups production (Calories-group, Emissions-group), and state crop production(Calories-crop, Emissions-crop), with different spatial levels of constraints. A maximum growth of 11% in calorie production is observed in Calories-nation while mitigating 2.5% emissions. Besides, the highest emission reduction of around 30% is observed in Emissions-group but with no change in calorie production. Emission scenarios can spare up to 14.8% land and 18.2% water, while calorie production-maximization scenarios can spare a maximum of 4.7% land and 6.5% water. The optimization-based methodology identifies the regions of altered land use by proposing appropriate crop substitution strategies, such as increasing oilseeds in Rajasthan and soybean in east Maharashtra. Many states show conservative production growth and emission reduction with state-level crop production targets (Calories-crop), suggesting crop redistribution within the state alone will not be sufficient unless improved technologies are introduced. The maximum growth and mitigation potential estimated in this study may be affected by climate shocks; therefore, introducing the improved technologies needs to be coupled with a crop redistribution mechanism to design climate-resilient and futuristic land use systems. The proposed land use model can be modified to incorporate climate change effects through consideration of scenarios of changed crop yields or through direct/indirect coupling with dynamic crop simulation models.

Influence of IoT implementation on Resource management in construction.

The desire to increase resource management efficacy in the construction sector is expanding because of measures to reduce costs, boost productivity, and minimize environmental impact. The Internet of Things (IoT) has the potential to alter resource management in the construction sector by delivering real-time data and insights that may assist decision-makers in optimizing resource allocation and usage. Incorporating Internet of Things (IoT) technology into the construction sector will be investigated in this study to discover how resource management is affected. The aim of the study is to identify the essential aspects that promote optimal IoT integration and to investigate how IoT may influence resource management. The relations between variables and their fundamental elements are investigated using structural equation modelling (SEM). In the context of building projects, the study analyses how IoT integration influences resource allocation and utilization, real-time monitoring, and proactive maintenance. The building sector in Malaysia provides concepts on IoT in resource management. Based on this research's outcomes, there is a distinct association between the utilization of IoT technology and effective resource management in the construction sector. IoT adoption is affected by a multiplicity of issues, including data analytics, data security and privacy, integration and interoperability, scalability, and flexibility. This study contributes to addressing considerable gaps in the corpus of information on IoT technology integration in the construction sector. It analyses how IoT may effect resource management, emphasizing how IoT technology may enhance the efficacy of human, mechanical, and material resources.

Reviving the forgotten legacy: Strategies for reviving qanats as sustainable solutions for agricultural water supply in arid and semi-arid regions.

In semi-arid and arid regions, qanats are a vital water source for agricultural needs. However, many communities are abandoning these traditional and sustainable water supply systems in favor of modern and more productive methods. Consequently, this research aimed to identify the most effective approaches for rejuvenating qanats in the dry and semi-arid areas of Razavi Khorasan province, Iran. To accomplish this goal, a comprehensive strategy was employed by merging the Strengths, Weaknesses, Opportunities, and Threats (SWOT, Best-Worst Method (BWM), and the Weighted Aggregated Sum Product Assessment (WASPAS).Based on interviews with experts in water resources and SWOT analysis, 27 factors affecting the restoration of qanats were identified and weighted with BWM. The results of BWM-WASPAS highlight the crucial role of a holistic approach in reviving qanats. The study findings reveal that the highest scores were obtained by the strategies reforming water governance policies to prioritize sustainable indigenous water resources (SO1) and establish an independent institution for qanat restoration (WT7). Conversely, educational (ST1), informational (SO3), and research (WO4) strategies concerning the revival of qanats were categorized as intermediate priorities. Lastly, strategies associated with farm-level support policies were assigned lower priorities. Accordingly, it is recommended that policymakers in Mashhad give priority to macro-level and institutional policies concerning qanat restoration, considering the current state of indigenous water resources. Additionally, a continued emphasis on research policies and farm-level initiatives is advised.

Effects of delay and error in the feedback structure of ecological management.

Ecosystems face various emergent uncertainties owing to factors such as climate change and accelerating anthropogenic impacts. Uncertainty is a major challenge and a barrier that ecosystem management faces, because it is difficult to precisely predict a priori risks that can have significant impacts on ecosystems. Hence, management with adaptive capacity is recommended to deal with such uncertainties, and feedback structures are central mechanisms for such flexible management. This study used mathematical models to clarify the specific impacts of feedback structures on ecosystem management, such as resource and wildlife management. In particular, the impact of errors in estimating ecosystem status when providing feedback and the impact of the time lag before feedback effects were implemented into management were examined. Overestimation of ecosystem status or a large time lag led to undesirable temporal oscillations in ecosystem status. However, these scenarios can be avoided when combined with management practices that limit the impact of management on the ecosystem, such as input control. Ecosystem management tends to have a large spatiotemporal scale, and implementing highly accurate monitoring and sophisticated feedback structures is difficult. However, the results suggest that effective ecosystem management with a simple feedback structure can be achieved through such complementary institutional design.

Comprehensive review of technologies for separate digestate treatment and agricultural valorisation within circular and green economy.

Anaerobic digestion (AD) has the potential to catalyse the shift from a linear to a circular economy. However, effective treatment and management of both solid (DSF) and liquid (DLF) digestate fraction treatment and management require adopting sustainable technologies to recover valuable by-products like energy, biofuels, biochar, and nutrients. This study reviews state-of-the-art advanced technologies for DSF and DLF treatment and valorisation, using life cycle assessment (LCA) and techno-economic analysis (TEA) in integrated digestate management (IDM). Key findings highlight these technologies' potential in mitigating environmental impacts from digestate management, but there's a need to improve process efficiency, especially at larger scales. Future research should prioritize cost-effective and eco-friendly IDM technologies. This review emphasizes how LCA and TEA can guide decision-making and promote sustainable agricultural practices. Ultimately, sustainable IDM technologies can boost resource recovery and advance circular economy principles, enhancing the environmental and economic sustainability of AD processes.

A flexible consistent framework for modelling multiple interacting environmental responses to management in space and time.

Management of resources is often a large-scale task addressed using many small-scale interventions. The range of scales at which organisms respond to those interventions, along with the many outcomes which management aims to achieve can make determining the success of management complex. Environmental flow is an example of management where there is a recognized need for managers to demonstrate the impact of their actions by integrating different types of environmental responses. Here, we aim to support decision making in environmental management via the development of a new modelling framework (eFlowEval). It has the capacity to capture best-available knowledge, to scale it in space and time, explore interactions among species, compare scenarios, and account for uncertainty. Thus, it provides a basis for including multiple target groups in a common system. The framework is readily updatable as new information becomes available and can identify where data are insufficient to be scientifically robust. We demonstrate the eFlowEval framework using three very different environmental responses: 1) metabolism, which is a measure of the energy produced and then used in an ecosystem, 2) favorability for a bird species of interest (royal spoonbill Platalea regia), and 3) competing wetland plants (Centipeda cunninghamii and lippia Phyla canescens). These demonstrations illustrate the capability of the eFlowEval framework but the specific outputs shown here should not be used to assess environmental responses to management. Using these demonstrations, we illustrate the capacity of the eFlowEval framework to provide assessments across a range of scales (local to landscape) and from short time frames (weeks to months) to multi-year assessments. Further, we illustrate the ability to: i) scale responses from local to basin scales, ii) vary driver-response model types, iii) represent uncertainty, iv) compare scenarios, v) accommodate variable parameter values at different locations, and vi) incorporate spatial and temporal dependencies and dependencies among species. We also illustrate the framework's ability to capture inter- and intraspecific interactions and their impact in space and time. The eFlowEval framework extends the capacity of the component response models to provide novel modeling capabilities for management at scale. It allows for interactions among species or processes to be incorporated, as well as in space and time. A large degree of flexibility is offered by the framework, in terms of driver-response model types, input data, and aggregation methods. Thus, the eFlowEval framework provides a mechanism to enhance the transparency of environmental watering decision making, capture institutional knowledge, enhance adaptive management and undertake evaluation of the impact of environmental watering at a range of spatial and temporal scales.

Slice-aware 5G network orchestration framework based on dual-slice isolation and management strategy (D-SIMS).

Network slicing is crucial to the 5G architecture because it enables the virtualization of network resources into a logical network. Network slices are created, isolated, and managed using software-defined networking (SDN) and network function virtualization (NFV). The virtual network function (VNF) manager must devise strategies for all stages of network slicing to ensure optimal allocation of physical infrastructure (PI) resources to high-acceptance virtual service requests (VSRs). This paper investigates two independent network slicing frameworks named as dual-slice isolation and management strategy (D-SIMS) and recommends the best of the two based on performance measurements. D-SIMS places VNFs for network slicing using self-sustained resource reservation (SSRR) and master-sliced resource reservation (MSRR), with some flexibility for the VNF manager to choose between them based on the degree to which the underlying physical infrastructure has been sliced. The present research work consists of two phases: the first deals with the creation of slices, and the second with determining the most efficient way to distribute resources among them. A deep neural network (DNN) technique is used in the first stage to generate slices for both PI and VSR. Then, in the second stage, we propose D-SIMS for resource allocation, which uses both the fuzzy-PROMETHEE method for node mapping and Dijkstra's algorithm for link mapping. During the slice creation phase, the proposed DNN training method's classification performance is evaluated using accuracy, precision, recall, and F1 score measures. To assess the success of resource allocation, metrics such as acceptance rate and resource effectiveness are used. The performance benefit is investigated under various network conditions and VSRs. Finally, to demonstrate the importance of the proposed work, we compare the simulation results to those in the academic literature.

Using lake sediments to assess the long-term impacts of anthropogenic activity in tropical river deltas.

Tropical river deltas, and the social-ecological systems they sustain, are changing rapidly due to anthropogenic activity and climatic change. Baseline data to inform sustainable management options for resilient deltas is urgently needed and palaeolimnology (reconstructing past conditions from lake or wetland deposits) can provide crucial long-term perspectives needed to identify drivers and rates of change. We review how palaeolimnology can be a valuable tool for resource managers using three current issues facing tropical delta regions: hydrology and sediment supply, salinisation and nutrient pollution. The unique ability of palaeolimnological methods to untangle multiple stressors is also discussed. We demonstrate how palaeolimnology has been used to understand each of these issues, in other aquatic environments, to be incorporated into policy. Palaeolimnology is a key tool to understanding how anthropogenic influences interact with other environmental stressors, providing policymakers and resource managers with a 'big picture' view and possible holistic solutions that can be implemented.

Decision making in human resource management: a systematic review of the applications of analytic hierarchy process.

The Analytic Hierarchy Process (AHP), Fuzzy Analytic Hierarchy Process (FAHP), and Analytic Network Process (ANP) methods are widely used for decision-making across various fields, and have shown success in numerous applications within human resource management (HRM). The purpose of this study is to present a systematic literature review on the applications of AHP, FAHP, and ANP in human resource management. The search process encompassed two main online databases, Scopus and Web of Science. This research covers a total of 180 application papers. To help readers extract quick and meaningful information, references are summarized in tabular format. The results showed that these methods have been applied in various domains of HRM such as performance management and appraisal, selecting human resources, talent attraction and retention, intellectual capital, workplace safety, reward management, e-HRM, green HRM, etc. To the best of our knowledge, no comprehensive research review has yet been conducted on the applications of AHP in HRM.

Traditional Machine Learning, Deep Learning, and BERT (Large Language Model) Approaches for Predicting Hospitalizations From Nurse Triage Notes: Comparative Evaluation of Resource Management.

BACKGROUND: Predicting hospitalization from nurse triage notes has the potential to augment care. However, there needs to be careful considerations for which models to choose for this goal. Specifically, health systems will have varying degrees of computational infrastructure available and budget constraints. OBJECTIVE: To this end, we compared the performance of the deep learning, Bidirectional Encoder Representations from Transformers (BERT)-based model, Bio-Clinical-BERT, with a bag-of-words (BOW) logistic regression (LR) model incorporating term frequency-inverse document frequency (TF-IDF). These choices represent different levels of computational requirements. METHODS: A retrospective analysis was conducted using data from 1,391,988 patients who visited emergency departments in the Mount Sinai Health System spanning from 2017 to 2022. The models were trained on 4 hospitals' data and externally validated on a fifth hospital's data. RESULTS: The Bio-Clinical-BERT model achieved higher areas under the receiver operating characteristic curve (0.82, 0.84, and 0.85) compared to the BOW-LR-TF-IDF model (0.81, 0.83, and 0.84) across training sets of 10,000; 100,000; and ~1,000,000 patients, respectively. Notably, both models proved effective at using triage notes for prediction, despite the modest performance gap. CONCLUSIONS: Our findings suggest that simpler machine learning models such as BOW-LR-TF-IDF could serve adequately in resource-limited settings. Given the potential implications for patient care and hospital resource management, further exploration of alternative models and techniques is warranted to enhance predictive performance in this critical domain. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1101/2023.08.07.23293699.

Developing environmental sustainability policy - How financing and subsidies are influencing green innovation in mineral extraction industries in China.

Mineral extraction is regarded as a pollution-intensive industry and is confronted with multiple environmental sustainability challenges. This issue poses an existential crisis for mineral extraction due to continued global pressure to adopt more sustainable practices in their functioning. Despite its importance, there is a notable gap in the literature regarding how these companies encounter financial challenges, particularly in the context of high-risk, long development cycles, and the unique double externalities associated with green innovation. This research, leveraging panel data from 2016 to 2023, aims to fill this gap by examining whether Chinese mineral extraction companies demonstrate distinct preferences for specific financing sources and by evaluating the role of government in facilitating their green innovation activities. Our findings indicate that such companies utilize a mix of internal and external financing to support their green innovation projects. It is observed that the influence of external finances channels, namely government subsidies, equity financing and debt financing, on green innovation progressively weakens, a conclusion supported by multiple robustness checks. Furthermore, the study highlights the crucial role of government subsidies in motivating publicly listed companies to enhance their green innovation activities through debt and equity financing, thereby contributing to a more equitable and sustainable development paradigm in the Global South.

Embracing the sustainable horizons through bioenergy innovations: a path to a sustainable energy future.

The urgent need for mitigating climate change necessitates a transformative shift in energy production and consumption paradigms. Amidst this challenge, bioenergy emerges as a pivotal contributor to the global energy transition, offering a diverse array of solid, liquid, and gaseous fuels derived from biomass. This mini review delves into the unique potential of bioenergy innovations, particularly renewable diesel, bio jet fuel, and ethanol, to reduce greenhouse gas emissions and transform various industries. The article highlights critical technological advancements, supportive policies, and cross-sector collaboration essential for a sustainable energy transition. Specific challenges such as ensuring a consistent biomass feedstock supply, decentralizing processing units, and navigating complex regulatory frameworks are examined. Innovative solutions like decentralized biomass processing and enhanced biomass logistics are discussed as pathways to overcome these barriers. The review provides specific recommendations for near-term policies and strategies to support decentralized facilities, showcasing bioenergy's role in achieving a sustainable future.

The red hat - designating leadership using visual and verbal cues: a mixed-methods study.

BACKGROUND: During a critical event in the labor and delivery operating room, it is crucial for team members responding to the situation to be aware of the designated leaders. Visual and verbal cues have been utilized to designate leadership in various healthcare settings; however, previous research has indicated mixed results using visual cues for role designation. METHODS: The purpose of this study was to explore the use of the red surgical hat as a visual cue of leadership during obstetric emergency simulation training. We used a mixed-methods design to analyze simulation-based education video and debriefing transcripts. RESULTS: There was a statistically significant difference in the proportion of participants who declared leadership vs. those who donned the red hat. Participants were more likely to visually declare leadership utilizing a red surgical bouffant hat than to verbally declare leadership. Most participants indicated that observing the red hat to detect leadership in the operating room was more effective than when leaders used a verbal declaration to inform others who was leading. CONCLUSIONS: Our findings suggest that utilizing a visual cue of leadership with the red surgical bouffant hat improves participant perceptions of communication of the surgical team during an obstetrical critical event in a simulation environment.

Evaluating associations between patient-to-nurse ratios and mortality, process of care events and vital sign documentation on paediatric wards: a secondary analysis of data from the EPOCH cluster-randomised trial.

OBJECTIVE: To describe the associations between patient-to-nurse staffing ratios and rates of mortality, process of care events and vital sign documentation. DESIGN: Secondary analysis of data from the evaluating processes of care and outcomes of children in hospital (EPOCH) cluster-randomised trial. SETTING: 22 hospitals caring for children in Canada, Europe and New Zealand. PARTICIPANTS: Eligible hospitalised patients were aged>37 weeks and <18 years. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was all-cause hospital mortality. Secondary outcomes included five events reflecting the process of care, collected for all EPOCH patients; the frequency of documentation for each of eight vital signs on a random sample of patients; four measures describing nursing perceptions of care. RESULTS: A total of 217 714 patient admissions accounting for 849 798 patient days over the course of the study were analysed. The overall mortality rate was 1.65/1000 patient discharges. The median (IQR) number of patients cared for by an individual nurse was 3.0 (2.8-3.6). Univariate Bayesian models estimating the rate ratio (RR) for the patient-to-nurse ratio and the probability that the RR was less than one found that a higher patient-to-nurse ratio was associated with fewer clinical deterioration events (RR=0.88, 95% credible interval (CrI) 0.77-1.03; P (RR<1)=95%) and late intensive care unit admissions (RR=0.76, 95% CrI 0.53-1.06; P (RR<1)=95%). In adjusted models, a higher patient-to-nurse ratio was associated with lower hospital mortality (OR=0.77, 95% CrI=0.57-1.00; P (OR<1)=98%). Nurses from hospitals with a higher patient-to-nurse ratio had lower ratings for their ability to influence care and reduced documentation of most individual vital signs and of the complete set of vital signs. CONCLUSIONS: The data from this study challenge the assumption that lower patient-to-nurse ratios will improve the safety of paediatric care in contexts where ratios are low. The mechanism of these effects warrants further evaluation including factors, such as nursing skill mix, experience, education, work environment and physician staffing ratios. TRIAL REGISTRATION NUMBER: EPOCH clinical trial registered on clinical trial.gov NCT01260831; post-results.

Work-life balance Twitter insights: A social media analysis before and after COVID-19 pandemic.

This research examines the perceptions of Twitter users regarding the prevalent topics within Work-Life Balance communication before and after the COVID-19 pandemic. The pressing questions surrounding current labour market drivers are addressed, particularly regarding the ongoing Fourth Industrial Revolution and the COVID-19 pandemic's impact on communicated themes, particularly in the Human Resource Management field, where Work-Life Balance has emerged as a key concept. Social media platforms like Twitter are pivotal in fostering discussions on Work-Life Balance in society. Over the past decade, Twitter has evolved into a significant research platform researchers utilise in more than ten thousand research articles. The online discourse on Twitter raises awareness of the importance of balancing work and personal life. The COVID-19 pandemic has unveiled new facets of Work-Life Balance, with social media as a key platform for discussing these issues. This research uses Social Media Analysis based on the Hashtag Research framework. A total of 1,768,628 tweets from 499,574 users were examined, and frequency, topic, and sentiment analysis were conducted. Pre-pandemic, the most communicated Work-Life Balance topics were performance and time management, while recruitment and employee development were identified post-pandemic. Pre-pandemic, the highest proportion of negative sentiment was time management and mental health prevention, shifting to time, employee development, and mental health prevention post-pandemic. Despite the limitations of our research, a proposed redefinition of the concept is also presented, including a design for an integrated Work-Life Balance model based on topics communicated by Twitter users. Given the need for a more robust approach to redefining the concept and developing an integrative Work-Life Balance model, the article provides fresh insights for future research.