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Organizational use of information and communications technologies (ICT) is increasingly resulting in negative cognitions in individuals, such as information overload and interruptions. Recent literature has encapsulated these cognitions in the concept of technostress, which is stress caused by an inability to cope with the demands of organizational computer usage. Given the critical role of the user in organizational information processing and accomplishing application-enabled workflows, understanding how these cognitions affect users' satisfaction with ICT and their performance in ICT-mediated tasks is an important step in appropriating benefits from current computing environments. The objective of this paper is to (1) understand the negative effects of technostress on the extent to which end users perceive the applications they use to be satisfactory and can utilize them to improve their performance at work and (2) identify mechanisms that can mitigate these effects. Specifically, we draw from the end-user computing and technostress literature to develop and validate a model that analyzes the effects of factors that create technostress on the individual's satisfaction with, and task performance using, ICT. The model also examines how user involvement in ICT development and support mechanisms for innovation can be used to weaken technostress-creating factors and their outcomes. The results, based on survey data analysis from 233 ICT users from two organizations, show that factors that create technostress reduce the satisfaction of individuals with the ICT they use and the extent to which they can utilize ICT for productivity and innovation in their tasks. Mechanisms that facilitate involvement of users, and encourage them to take risks, learn, explore new ideas, and experiment in the context of ICT use, diminish the factors that create technostress and increase satisfaction with the ICT they use. These mechanisms also have a positive effect on users' appropriation of ICT for productivity and innovation in their tasks. The paper contributes to emerging literature on negative outcomes of ICT use by (1) highlighting the influence of technostress on users' satisfaction and performance (i.e., productivity and innovation in ICT-mediated tasks) with ICT, (2) extending the literature on technostress, which has so far looked largely at the general behavioral and psychological domains, to include the domain of end-user computing, and (3) demonstrating the importance of user involvement and innovation support mechanisms in reducing technostress-creating conditions and their ICT use-related outcomes.

Well designed and properly executed randomised trials are considered the most reliable evidence on the benefits of healthcare interventions. However, there is overwhelming evidence that the quality of reporting is not optimal. The CONSORT (Consolidated Standards of Reporting Trials) statement was designed to improve the quality of reporting and provides a minimum set of items to be included in a report of a randomised trial. CONSORT was first published in 1996, then updated in 2001 and 2010. Here, we present the updated CONSORT 2025 statement, which aims to account for recent methodological advancements and feedback from end users. We conducted a scoping review of the literature and developed a project-specific database of empirical and theoretical evidence related to CONSORT, to generate a list of potential changes to the checklist. The list was enriched with recommendations provided by the lead authors of existing CONSORT extensions (Harms, Outcomes, Non-pharmacological Treatment), other related reporting guidelines (TIDieR) and recommendations from other sources (eg, personal communications). The list of potential changes to the checklist was assessed in a large, international, online, three-round Delphi survey involving 317 participants and discussed at a two-day online expert consensus meeting of 30 invited international experts. We have made substantive changes to the CONSORT checklist. We added seven new checklist items, revised three items, deleted one item, and integrated several items from key CONSORT extensions. We also restructured the CONSORT checklist, with a new section on open science. The CONSORT 2025 statement consists of a 30-item checklist of essential items that should be included when reporting the results of a randomised trial and a diagram for documenting the flow of participants through the trial. To facilitate implementation of CONSORT 2025, we have also developed an expanded version of the CONSORT 2025 checklist, with bullet points eliciting critical elements of each item. Authors, editors, reviewers, and other potential users should use CONSORT 2025 when writing and evaluating manuscripts of randomised trials to ensure that trial reports are clear and transparent.

AbstractBackgroundWell designed and properly executed randomised trials are considered the most reliable evidence on the benefits of healthcare interventions. However, there is overwhelming evidence that the quality of reporting is not optimal. The CONSORT (Consolidated Standards of Reporting Trials) statement was designed to improve the quality of reporting and provides a minimum set of items to be included in a report of a randomised trial. CONSORT was first published in 1996, then updated in 2001 and 2010. Here, we present the updated CONSORT 2025 statement, which aims to account for recent methodological advancements and feedback from end users.MethodsWe conducted a scoping review of the literature and developed a project-specific database of empirical and theoretical evidence related to CONSORT, to generate a list of potential changes to the checklist. The list was enriched with recommendations provided by the lead authors of existing CONSORT extensions (Harms, Outcomes, Non-pharmacological Treatment), other related reporting guidelines (TIDieR) and recommendations from other sources (eg, personal communications). The list of potential changes to the checklist was assessed in a large, international, online, three-round Delphi survey involving 317 participants and discussed at a two-day online expert consensus meeting of 30 invited international experts.ResultsWe have made substantive changes to the CONSORT checklist. We added seven new checklist items, revised three items, deleted one item, and integrated several items from key CONSORT extensions. We also restructured the CONSORT checklist, with a new section on open science. The CONSORT 2025 statement consists of a 30-item checklist of essential items that should be included when reporting the results of a randomised trial and a diagram for documenting the flow of participants through the trial. To facilitate implementation of CONSORT 2025, we have also developed an expanded version of the CONSORT 2025 checklist, with bullet points eliciting critical elements of each item.ConclusionAuthors, editors, reviewers, and other potential users should use CONSORT 2025 when writing and evaluating manuscripts of randomised trials to ensure that trial reports are clear and transparent.

BackgroundThe AIDS Healthcare Foundation (AHF-Checkpoint) in the Netherlands provides free ‘walk-in’, rapid HIV testing for key populations at onsite and pop-up locations. We explored the perspectives, experiences, and needs of lay providers and end users of this HIV test service, taking into account the WHO 5Cs (consent, confidentiality, counselling, correct results, connection-to-care) performance criteria for HIV test services.MethodsA qualitative evaluation with 15 semi-structured in-depth interviews (April-June 2020) by telephone or video calling with ten lay providers and five end users from AHF-Checkpoint. Recorded interviews were transcribed verbatim and thematically analysed.ResultsData analysis identified four domains: 1) accessibility of HIV testing, 2) quality of test procedures, 3) bridging (transitional care), and 4) future strategies for service delivery. AHF-Checkpoint fills a gap for key populations, including LGBTQ and refugees, who experience barriers to HIV testing at sexual health centres or GPs by providing anonymous, rapid testing. The level of trust between lay providers and end users was highly valued by end users. They also appreciated the low threshold to test, no waiting lists, no test costs or triaging that could include referral to another test location. Needs expressed by lay providers included more preparedness for emotionally charged situations and extra training to improve knowledge on STIs. Some end users expressed a need for a full STI test package at AHF-Checkpoint. Of the 5Cs, consent, counselling, and correct results were realised, but confidentiality was sometimes difficult to achieve at pop-up locations, and referral barriers for confirmation testing (connection-to-care) were occasionally experienced by lay providers during weekends.ConclusionAHF-Checkpoint was described as a convenient and easily accessible service by end users and lay providers. Of the WHO 5Cs, connection-to-care could be optimised to ensure HIV confirmation -and STI testing through a liaison approach with professionals from the regular healthcare sector.

Machine learning is disruptive. At the same time, machine learning can only succeed by collaboration among many parties in multiple steps naturally as pipelines in an eco-system, such as collecting data for possible machine learning applications, collaboratively training models by multiple parties and delivering machine learning services to end users. Data are critical and penetrating in the whole machine learning pipelines. As machine learning pipelines involve many parties and, in order to be successful, have to form a constructive and dynamic eco-system, marketplaces and data pricing are fundamental in connecting and facilitating those many parties. In this article, we survey the principles and the latest research development of data pricing in machine learning pipelines. We start with a brief review of data marketplaces and pricing desiderata. Then, we focus on pricing in three important steps in machine learning pipelines. To understand pricing in the step of training data collection, we review pricing raw data sets and data labels. We also investigate pricing in the step of collaborative training of machine learning models and overview pricing machine learning models for end users in the step of machine learning deployment. We also discuss a series of possible future directions.

Quantum key distribution (QKD) 1 , 2 has the potential to enable secure communication and information transfer 3 . In the laboratory, the feasibility of point-to-point QKD is evident from the early proof-of-concept demonstration in the laboratory over 32 centimetres 4 ; this distance was later extended to the 100-kilometre scale 5 , 6 with decoy-state QKD and more recently to the 500-kilometre scale 7 – 10 with measurement-device-independent QKD. Several small-scale QKD networks have also been tested outside the laboratory 11 – 14 . However, a global QKD network requires a practically (not just theoretically) secure and reliable QKD network that can be used by a large number of users distributed over a wide area 15 . Quantum repeaters 16 , 17 could in principle provide a viable option for such a global network, but they cannot be deployed using current technology 18 . Here we demonstrate an integrated space-to-ground quantum communication network that combines a large-scale fibre network of more than 700 fibre QKD links and two high-speed satellite-to-ground free-space QKD links. Using a trusted relay structure, the fibre network on the ground covers more than 2,000 kilometres, provides practical security against the imperfections of realistic devices, and maintains long-term reliability and stability. The satellite-to-ground QKD achieves an average secret-key rate of 47.8 kilobits per second for a typical satellite pass—more than 40 times higher than achieved previously. Moreover, its channel loss is comparable to that between a geostationary satellite and the ground, making the construction of more versatile and ultralong quantum links via geosynchronous satellites feasible. Finally, by integrating the fibre and free-space QKD links, the QKD network is extended to a remote node more than 2,600 kilometres away, enabling any user in the network to communicate with any other, up to a total distance of 4,600 kilometres. A quantum network that combines 700 fibre and two ground-to-satellite links achieves quantum key distribution between more than 150 users over a combined distance of 4,600 kilometres.

Objective The aim of this study was to describe the process used to develop a theory‐based, online fall prevention self‐management programme for ambulatory and non‐ambulatory people with multiple sclerosis (pwMS). Methods The development process was guided by the Medical Research Council framework of complex interventions and began with a scoping review of the literature on self‐management of falls in pwMS. Subsequent phases of development were performed through iterative and concurrent processes and were informed by the perspectives of pwMS and healthcare professionals with MS expertise. Results Through a systematic and iterative process in close collaboration with pwMS and healthcare professionals, a theory‐based online fall prevention self‐management programme, Fewer Falls in MS, for ambulatory and non‐ambulatory pwMS was developed. The programme is grounded in theory and pedagogical models and features utilization of action plans to address diverse influences on fall risks. Conclusions A carefully operationalized definition of self‐management and an iterative co‐development process were essential to the creation of the Fewer falls in MS programme. Continuation of the co‐development process and collaboration with end users was needed to refine the programme. Patient or Public Contribution PwMS and healthcare professionals were involved throughout the development process of the programme. The patient organization Neuro Sweden was contacted in the initial phase to discuss the relevance of a self‐management programme to prevent falls in MS. They supported the research group (all authors) in identification of and contact with pwMS with interest to participate. Three members of the research group (S.T.J., M.F. and C.Y.), that is, the operative group, met neuro Sweden and one pwMS to further discuss the relevance of a self‐management programme to prevent falls. To develop the process and content of the fall prevention programme, a co‐design process was performed together with pwMS and healthcare professionals. The results of the co‐design process are presented in this manuscript. In addition to participating in the co‐design process, pwMS and healthcare professionals provided feedback to the research group on programme process and content on several occasions during the subsequent programme development process. In a pretest (Beta version) of the programme, four pwMS acted as test subjects and provided additional feedback on the programme to the research group. Trial Registration NCT04317716.

Dramatic success in machine learning has led to a new wave of AI applications (for example, transportation, security, medicine, finance, defense) that offer tremendous benefits but cannot explain their decisions and actions to human users. DARPA's explainable artificial intelligence (XAI) program endeavors to create AI systems whose learned models and decisions can be understood and appropriately trusted by end users. Realizing this goal requires methods for learning more explainable models, designing effective explanation interfaces, and understanding the psychologic requirements for effective explanations. The XAI developer teams are addressing the first two challenges by creating ML techniques and developing principles, strategies, and human‐computer interaction techniques for generating effective explanations. Another XAI team is addressing the third challenge by summarizing, extending, and applying psychologic theories of explanation to help the XAI evaluator define a suitable evaluation framework, which the developer teams will use to test their systems. The XAI teams completed the first of this 4‐year program in May 2018. In a series of ongoing evaluations, the developer teams are assessing how well their XAM systems' explanations improve user understanding, user trust, and user task performance.

The Global Wind Atlas (GWA) provides high-resolution databases and maps of the wind resource for all land points and for water points within 200 km of the coastline, excluding Antarctica. The GWA is used to identify and understand the global, national, regional, and local potential for wind energy and to guide energy specialists, policymakers, and planners in the transition to a sustainable energy system. This information is vital to ensuring the growth of wind energy, helping to transition to a sustainable energy system, which will mitigate climate change and meet the world’s need for reliable, affordable, and clean energy. The GWA uses the established numerical wind atlas methodology to downscale coarse-resolution wind data to microscale, using linearized flow modeling and high-resolution topographic data. There have been three versions of the GWA, each using mesoscale model data at successively higher spatial resolution. A website and geographic information system (GIS) files support quick and in-depth analysis. Validation data and analysis, using measurements from tall masts located worldwide, are also provided through the web application. The development process of the GWA involves a dialogue between meteorological modelers, wind energy development experts, web designers, and representatives of the end users to provide accurate data in a dynamic and relevant way. This article outlines the general method, specific development, and application of the Global Wind Atlas.

Statistical postprocessing techniques are nowadays key components of the forecasting suites in many national meteorological services (NMS), with, for most of them, the objective of correcting the impact of different types of errors on the forecasts. The final aim is to provide optimal, automated, seamless forecasts for end users. Many techniques are now flourishing in the statistical, meteorological, climatological, hydrological, and engineering communities. The methods range in complexity from simple bias corrections to very sophisticated distribution-adjusting techniques that incorporate correlations among the prognostic variables. The paper is an attempt to summarize the main activities going on in this area from theoretical developments to operational applications, with a focus on the current challenges and potential avenues in the field. Among these challenges is the shift in NMS toward running ensemble numerical weather prediction (NWP) systems at the kilometer scale that produce very large datasets and require high-density high-quality observations, the necessity to preserve space-time correlation of high-dimensional corrected fields, the need to reduce the impact of model changes affecting the parameters of the corrections, the necessity for techniques to merge different types of forecasts and ensembles with different behaviors, and finally the ability to transfer research on statistical postprocessing to operations. Potential new avenues are also discussed.