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Objective Study Setting and Design Data Sources and Analytic Sample Principal Findings Conclusions To investigate women's perceptions of telemedicine for reproductive health care services, focusing on how perceived benefits and privacy risks influence their intentions to adopt telemedicine and their willingness to disclose personal health information.A cross‐sectional survey was conducted. The study applied the privacy calculus theory to the context of telemedicine for reproductive health, using adapted, validated variables to develop the survey. Outcome variables included intentions to adopt telemedicine and willingness to disclose accurate personal health information.Data were collected in May and June 2023 using Qualtrics online panel services, targeting women across the United States who had not used telemedicine for reproductive health. The sample comprised 847 women aged 18 and older. Structural equation modeling was employed using AMOS v28.0 to test the hypothesized relationships between perceived benefits, perceived risks, and adoption intentions. The analysis controlled for age, household income, political affiliation, religious views, and prior births.Perceived benefits were positively related to intention to adopt telemedicine for reproductive care (β: 0.600, p < 0.001), and willingness to disclose accurate personal health information (β: 0.453, p < 0.001). Unexpectedly, perceived privacy risks were positively related to adoption intentions (β: 0.128, p < 0.001), but negatively related to willingness to disclose (β: −0.282, p < 0.001). Intentions to adopt were positively associated with willingness to disclose (β: 0.089, p < 0.05). Lastly, older women and women located in states with abortion restrictions expressed lower intentions to adopt. The model explained 40.2% of variance in intention to adopt and 38.3% of variance in willingness to disclose.The study demonstrates the importance of perceived benefits and privacy risks in driving telemedicine adoption and disclosure intentions among women in the reproductive health context. These findings suggest the need for targeted strategies to address privacy concerns and support telemedicine adoption, particularly in restrictive regulatory environments. [ABSTRACT FROM AUTHOR]

Exploring online criticisms of the "take the knee" protest during "Euro 2020", this article examines how alt- and far-right conspiracies were both constructed and communicated via the social media platform, Twitter. By providing a novel exploration of alt-right conspiracies during an international football tournament, a qualitative thematic analysis of 1,388 original tweets relating to Euro 2020 was undertaken. The findings reveal how, in criticisms levelled at both "wokeism" and the Black Lives Matter movement, anti-white criticisms of the "take the knee" protest were embroiled in alt-right conspiracies that exposed an assumed Cultural Marxist, "woke agenda" in the tournament's organisation and mainstream media coverage. In conclusion, it is argued that conspiratorial discourses, associated with the alt-right, provided a framework through which the protest could be understood. This emphasises how the significance of conspiracy functions to promote the wider dissemination of alt-right ideology across popular cultural contexts, such as sport. [ABSTRACT FROM AUTHOR]

Snakebite envenomations (SBE) are a significant global public health threat due to their morbidity and mortality. This is a neglected public health issue in many tropical and subtropical countries. Brazil is in the top ten countries affected by SBE, with 32,160 cases reported only in 2020, posing a high burden for this population. In this paper, we describe the data structure of snakebite records from 2007 to 2020 in the Notifiable Disease Information System (SINAN), made available by the Brazilian Ministry of Health (MoH). In addition, we also provide R scripts that allow a quick and automatic updating of data from the SINAN according to its availability. The data presented in this work are related to clinical and demographic information on SBE cases. Also, data on outcomes, laboratory results, and treatment are available. The dataset is available and freely accessible; however, preprocessing, adjustments, and standardization are necessary due to incompleteness and inconsistencies. Regardless of these limitations, it provides a solid basis for assessing different aspects and the national burden of envenoming. Dataset: The dataset is available at https://github.com/LabdataManaus/describe%5fsnakebite/tree/main. Accessed on 26 April 2024. Dataset License: CC BY-NC-SA [ABSTRACT FROM AUTHOR]

The term sportswashing has been discussed and analysed within academic circles, as well as the mainstream media. However, the majority of existing research has focused on one-off event-based sportswashing strategies (such as autocratic states hosting major international sports events) rather than longer term investment-based strategies (such as state actors purchasing sports clubs and teams). Furthermore, little has been written about the impact of this latter strategy on the existing fanbase of the purchased team and on their relationship with sportswashing and the discourses surrounding it. This paper addresses this lacuna through analysis of a popular Manchester City online fan forum, which illustrates the manner in which this community of dedicated City fans have legitimated the actions of the club's ownership regime, the Abu Dhabi United Group – a private equity group operated by Abu Dhabi royalty and UAE politicians. The discursive strategies of the City fans are discussed, in addition to the wider significance of these strategies on the issue of sportswashing and its coverage by the media. [ABSTRACT FROM AUTHOR]

This paper considers agentic-activities relating to the Irish language on Twitter and who is engaged with them? To answer this question, this study draws on Twitter data and filters it using the linguistic identifiers, '#Gaeilge' and the word 'Gaeilge' (Irish language). Tweets with these identifiers are conceptually considered as formulations of agentic practice with respect to language (Sabatier, P. [2007]. "Theories of the Policy Process." Boulder, CO: Westview Press). Individuals who use the #Gaeilge identifier in particular are viewed within this study as micro-implementers. Their communications provide partial insight into agentic activities within the Twittersphere specifically, related to and about the Irish language. To explore this data, peak, network, content and sentiment analyses were conducted. The study's findings illustrate that there are a number of influential hubs in this network. They are in the main micro-level individuals as opposed to macro-level or governmental institutions promoting the Irish language. This paper demonstrates the potential for in-depth research into agentic practices of minority linguistic communities within Twitter, and outlines the limitations of a computational approach in conducting such research.

England's 'shock' exit from the Euro 2016 Football Championship and the UK electorate's decision to (Br)exit from the EU occurred almost simultaneously, providing an interesting lens through which to examine unfolding tensions in the UK and its component identities – all the more so given the presence of two of the three other component countries of the UK, Wales and Northern Ireland, at the Championship. Our analysis of 34,324 original tweets featuring both the hashtag #Euro2016 and #Brexit shows a clear tendency to conflate British and English identities in the context of Brexit, a conflation coloured by largely negative characteristics. We pay particular attention to how recurring themes concerning leadership, accountability and immigration are articulated in the context of both sporting and political events. In addition, we examine the significance of the structural logic of social media in these discussions of the sport-nation nexus, with reference to vortextuality and mediatization. [ABSTRACT FROM AUTHOR]

Trust is widely regarded as being foundational in workplace relationships. The violation of interpersonal trust results in a range of negative affective, cognitive and behavioural consequences for the injured party. However, research has yet to isolate the specific neural areas and processes activated when different types of interpersonal trust are breached. Using electroencephalogram with 68 participants, we identified the effects of three distinct types of trust violations—ability violation, integrity violation and benevolence violations—on electrical brain activity. Our findings indicate that trust violations are processed in social cognitive‐related brain areas. Specifically, our results identify the significance of the default mode network (DMN), relevant to the processing of social information, in trust violation and further isolated distinct activity for ability, integrity and benevolence trust violation, with integrity violations demonstrating the greatest reaction in the DMN. Benevolence violations generated the next greatest reaction but were not significantly different from the ability violations. This potential distinction may be worth further investigation in future research. Our findings highlight the potential importance of the DMN in processing cues regarding the trustworthiness of others and the distinctiveness of the processing of violation cues of the three facets of trustworthiness. [ABSTRACT FROM AUTHOR]

This study examines public policy advocacy by pediatricians and other health professionals in the hashtag community: #putkids1st. The study explores 4321 tweets that feature the hashtag, generated by 1231 unique users largely drawn from the American Association of Pediatricians and its members. The data are used to explore the structural dynamics of the hashtag community, the role of homophily, and to test a source-message framework to predict and recommendations to help improve engagement and retransmission of professional health advocacy messages. [ABSTRACT FROM AUTHOR]

The rise of online hate speech in sports is a growing concern, with fans, players and officials subject to racist, sexist and homophobic abuse (in addition to many other prejudices) via social media platforms. While hate speech and discrimination have always been problems in sports, the growth of social media has seen them exacerbated exponentially. As a consequence, policy makers, sport governing bodies and grassroots anti-hate organisations are largely left playing catch-up with the rapidly shifting realm of online hate. Scholars have attempted to fill this vacuum with research into this topic, but such is the evolving nature of the issue that research has been diverse and fragmentary. We offer a scoping review into the scholarship of online hate in sport in order to encourage and facilitate further research into this urgent issue. Our review will achieve this through offering a comprehensive cataloguing of previously employed methodologies, case studies and conclusions. In doing so, it will not only equip future researchers with a concise overview of existing research in the field, but also illuminate areas and approaches in need of further examination. [ABSTRACT FROM AUTHOR]

Streets perform a number of important functions and have a wide range of activities performed in them. There is a small but growing focus on streets as a more generalisable, atomised, and therefore more manageable unit of development and analysis than cities. Despite the public realm being one of the largest physical spaces on streets, the impact and potential of digitalisation projects on this realm is rarely considered. In this article, the smartness of a street is derived from the cyber-physical social infrastructure in the public realm, including data obtained from sensors, the interconnection between different services, technologies and social actors, intelligence derived from analysis of the data, and optimisation of operations within a street. This article conceptualises smart streets as basic units of urban space that leverage cyber-physical social infrastructure to provide and enable enhanced services to and between stakeholders, and through stakeholders' use of the street, generate data to optimise its services, capabilities, and value to stakeholders. A proposed conceptual framework is used to identify and explore how streets can be augmented and create value through cyber-physical social infrastructure and digital enhancements. We conclude with a discussion of future avenues of research. [ABSTRACT FROM AUTHOR]

During the last decade, sustainable fashion has attracted increasing attention from policymakers, firms, and consumers. This interest is also reflected in consumer behavior research in this area. Notwithstanding this, there are few systematic literature reviews from a consumer behavior perspective. The purpose of this paper is to systematically review and critically assess the studies on consumer behavior in sustainable fashion to identify the research gap in this context and inform a future research agenda. A total of 167 journal articles were identified, and a final sample of 88 articles were synthesized. A descriptive analysis was performed to examine the research methods, research trends, and theoretical underpinnings. Separately, a synthetic analysis was performed using the Stimulus‐Organism‐Response (S‐O‐R) framework. Findings reveal a significant increase in the volume of publications since 2009. Qualitative, experimental, cross‐cultural, and longitudinal studies are significantly less represented in the literature. No evidence of research using big data techniques were identified. Much of the research published is not adequately grounded in theory. The findings also suggest that the consumer attitude‐behavior gap requires further exploration. This review fills a need to summarize the current state of consumer behavior in sustainable fashion literature and provides valuable insights into the existing marketing corpus on the topic. Moreover, the review identifies a number of outstanding research gaps that can inform future research. [ABSTRACT FROM AUTHOR]

As signals of internal control weaknesses, cyber security incidents can represent significant risk factors to the quality of financial reporting. We empirically assess the audit quality implications of data breaches for a large sample of US firms. Using a difference-in-difference approach based on a matched sample of breached and non-breached firms, we find no evidence that cyber-security incidents result in a decline in audit quality. Instead, we observe positive shifts in four widely-used proxies for audit quality. We document that breached firms (i) experience a decrease in abnormal accruals, (ii) are less likely to report small profits or small earnings increases, (iii) are more likely to be issued a going concern report, and (iv) are less likely to restate their financial statements in the two years following a breach. Our results indicate that auditors effectively offset increases in audit risk through additional substantive testing and audit effort. Our evidence supports the view that auditors have increased their audit risk awareness and put in place adequate procedures to deal with the consequences of cyber-security incidents. [ABSTRACT FROM AUTHOR]

Mobile contact tracing applications have emerged as a potential solution to track and reduce the transmission of viruses such as Covid‐19. These applications require the disclosure of potentially sensitive personal information thus generating understandable implications for personal privacy. This research aims to determine the factors driving acceptance of these applications, with acceptance represented by three distinct variables, namely usage intentions, willingness to disclose personal data, and willingness to rely on health advice. The study examines the influence of perceived privacy, social influence, and benefits on acceptance of contact tracing applications among a sample of 1,114 Brazilian citizens. The study leverages social contract theory to demonstrate the importance of perceived control and perceived surveillance in the formation of individuals' perceptions of privacy. Integrating privacy calculus theory with social contract theory to include reciprocity and social influence, our findings suggest that perceived privacy, reciprocal benefits, and social influence all positively influence individuals' intentions to download or continue the use of contact tracing applications, while intentions to disclose information are influenced by adoption intentions, perceived privacy, and reciprocal benefits and individuals' willingness to rely on contact tracing applications for health advice is influenced by reciprocal benefits and disclosure intentions. [ABSTRACT FROM AUTHOR]

Globalisation, technological advances, liberalisation of financial markets, and changing consumer behaviour are transforming banking profoundly. Under the EU Payment Services Directive 2 (PSD2), incumbent banks must open up their data, processes, and business functionalities to customers and third parties including rivals. It is critical to understand consumer behaviour post-PSD2, and the potential impact of PSD2 on the functioning of the retail banking and financial services market. In this preliminary study of 244 consumers from six European countries, we explore the role of social influence, facilitating conditions, perceived risk, and effort and performance expectancy in order to unravel the determinants of consumers' acceptance of account information services (AIS) as provisioned under PSD2, which provide consolidated bank account information for consumers with multiple bank accounts across multiple banking institutions. Our findings suggest that the competing influences of (a) positive perceptions such as social influence, facilitating conditions, and performance expectancy, and (b) negative perceptions related to risk, sway consumers' intentions to adopt AIS. [ABSTRACT FROM AUTHOR]

Public health interventions to counter the COVID-19 pandemic have accelerated and increased digital adoption and use of the Internet for sourcing health information. Unfortunately, there is evidence to suggest that it has also accelerated and increased the spread of false information relating to COVID-19. The consequences of misinformation, disinformation and misinterpretation of health information can interfere with attempts to curb the virus, delay or result in failure to seek or continue legitimate medical treatment and adherence to vaccination, as well as interfere with sound public health policy and attempts to disseminate public health messages. While there is a significant body of literature, datasets and tools to support countermeasures against the spread of false information online in resource-rich languages such as English and Chinese, there are few such resources to support Portuguese, and Brazilian Portuguese specifically. In this study, we explore the use of machine learning and deep learning techniques to identify fake news in online communications in the Brazilian Portuguese language relating to the COVID-19 pandemic. We build a dataset of 11,382 items comprising data from January 2020 to February 2021. Exploratory data analysis suggests that fake news about the COVID-19 vaccine was prevalent in Brazil, much of it related to government communications. To mitigate the adverse impact of fake news, we analyse the impact of machine learning to detect fake news based on stop words in communications. The results suggest that stop words improve the performance of the models when keeping them within the message. Random Forest was the machine learning model with the best results, achieving 97.91% of precision, while Bi-GRU was the best deep learning model with an F1 score of 94.03%. [ABSTRACT FROM AUTHOR]

Summary: Mobile network traffic prediction is an important input into network capacity planning and optimization. Existing approaches may lack the speed and computational complexity to account for bursting, non‐linear patterns, or other important correlations in time series mobile network data. We compare the performance of two deep learning (DL) architectures, long short‐term memory (LSTM) and gated recurrent unit (GRU), and two conventional machine learning (ML) architectures—Random Forest and Decision Tree—for predicting mobile Internet traffic using 2 months of Telecom Italia data for Milan. K‐Means clustering was used a priori to group cells based on Internet activity, and the Grid Search method was used to identify the best configurations for each model. The predictive quality of the models was evaluated using root mean squared error and mean absolute error. Both DL algorithms were effective in modeling Internet activity and seasonality, both within days and across 2 months. We find variations in performance across clusters within the city. Overall, the DL models outperformed the conventional ML models, and the LSTM outperformed the GRU in our experiments. [ABSTRACT FROM AUTHOR]

Background: Neglected tropical diseases (NTDs) primarily affect the poorest populations, often living in remote, rural areas, urban slums or conflict zones. Arboviruses are a significant NTD category spread by mosquitoes. Dengue, Chikungunya, and Zika are three arboviruses that affect a large proportion of the population in Latin and South America. The clinical diagnosis of these arboviral diseases is a difficult task due to the concurrent circulation of several arboviruses which present similar symptoms, inaccurate serologic tests resulting from cross-reaction and co-infection with other arboviruses. Objective: The goal of this paper is to present evidence on the state of the art of studies investigating the automatic classification of arboviral diseases to support clinical diagnosis based on Machine Learning (ML) and Deep Learning (DL) models. Method: We carried out a Systematic Literature Review (SLR) in which Google Scholar was searched to identify key papers on the topic. From an initial 963 records (956 from string-based search and seven from a single backward snowballing procedure), only 15 relevant papers were identified. Results: Results show that current research is focused on the binary classification of Dengue, primarily using tree-based ML algorithms. Only one paper was identified using DL. Five papers presented solutions for multi-class problems, covering Dengue (and its variants) and Chikungunya. No papers were identified that investigated models to differentiate between Dengue, Chikungunya, and Zika. Conclusions: The use of an efficient clinical decision support system for arboviral diseases can improve the quality of the entire clinical process, thus increasing the accuracy of the diagnosis and the associated treatment. It should help physicians in their decision-making process and, consequently, improve the use of resources and the patient's quality of life. Author summary: Neglected tropical diseases (NTDs) primarily affect the poorest populations, often living in remote, rural areas, urban slums or conflict zones. Arboviruses are a significant NTD category spread by mosquitoes. Dengue, Chikungunya, and Zika are three arboviruses that affect a large proportion of the population in Latin and South America. The clinical diagnosis of these arboviral diseases is a difficult task due to the concurrent circulation of several arboviruses which present similar symptoms and, sometimes, inaccurate test results. In this paper, we present the state of the art of studies investigating the automatic classification of arboviral diseases based on Machine Learning (ML) and Deep Learning (DL) models. Results show that current research is focused on the classification of Dengue, primarily using tree-based ML algorithms. The use of an efficient clinical decision support system for arboviral diseases can improve the quality of the entire clinical process, thus increasing the accuracy of the diagnosis and the associated treatment. It should help physicians in their decision-making process and, consequently, improve the use of resources and the patient's quality of life. [ABSTRACT FROM AUTHOR]

This study investigates the effect of news media coverage on trading activity in, and the liquidity of, target firms' shares around acquisition announcements. We use the number of articles published in four of the UK's main newspapers as a proxy for media coverage. Our dataset includes 350 UK domestic acquisition deals between 1996 and 2014. The results of our analysis suggest that media coverage is positively associated with target firms' trading activity and stock liquidity. This is consistent with the media playing a key role in mitigating information asymmetry in the financial markets. This study contributes to the literature on stock market reactions to acquisition announcements by investigating the effect of media coverage on trading activity and stock liquidity beyond the price run-up, and by providing additional insights into the UK market which traditionally attracts less attention than the US market. [ABSTRACT FROM AUTHOR]