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Despite the widespread use of distance learning during the coronavirus disease-2019 (COVID-19) pandemic, little is known about the developmental appropriateness of this instructional format for preschoolers, particularly when implemented in inclusive settings. The current research was implemented in a university-affiliated, state-funded inclusive preschool classroom focused on autism spectrum disorder (ASD) where parents were given the option to enroll in a full distance program (FDP) during the first 9 weeks of the 2020/21 school year. Parents of four children, including two children with ASD, selected the FDP option. Synchronous whole group, small group, and individual online sessions were recorded using screen capture and coded for children's maintained attention and directed communication. Further, parents and teachers completed exit interviews or a focus group, which were transcribed and analyzed using qualitative research methods. This is the first study to use observational methods to measure child engagement during preschool FDP sessions. Results from this mixed-method multiple case study paint a rich picture of both opportunities and limitations inherent in distance learning when implemented in inclusive preschool settings. General conclusions, future directions, and study limitations are discussed.
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OBJECTIVES: To investigate whether sentiment analysis and topic modelling can be used to monitor the sentiment and opinions of junior doctors. DESIGN: Retrospective observational study based on comments on a social media website. SETTING: Every publicly available comment in r/JuniorDoctorsUK on Reddit from 1 January 2018 to 31 December 2021. PARTICIPANTS: 7707 Reddit users who commented in the r/JuniorDoctorsUK subreddit. MAIN OUTCOME MEASURE: Sentiment (scored -1 to +1) of comments compared with results of surveys conducted by the General Medical Council. RESULTS: Average comment sentiment was positive but varied significantly during the study period. Fourteen topics of discussion were identified, each associated with a different pattern of sentiment. The topic with the highest proportion of negative comments was the role of a doctor (38%), and the topic with the most positive sentiment was hospital reviews (72%). CONCLUSION: Some topics discussed in social media are comparable to those queried in traditional questionnaires, whereas other topics are distinctive and offer insight into what themes junior doctors care about. Events during the coronavirus pandemic may explain the sentiment trends in the junior doctor community. Natural language processing shows significant potential in generating insights into junior doctors' opinions and sentiment.
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Coronavirus Infections , Social Media , Humans , Attitude , Coronavirus Infections/epidemiology , Medical Staff, Hospital , PandemicsABSTRACT
Older adults or the elderly are part of the group of peeople that make up a majority of the health burdens in society, and the outbreak of coronavirus or COVID - 19 has further placed them in a risky position due to their age. Going by the latter statement, the research examines the impact of social media on this population's online health information-seeking behaviour in the face of the global coronavirus health crisis in Nigeria. The study employed Krejcie and Morgan's (1970) technique for ascertaining the sample size for a given population, and arrived at a sample of 384 quantitatively surveyed through a questionnaire. The study concluded that social media positively impacted the health behaviour of the research population, and the information sought on social media is reliable, and it also influenced their behaviour positively. However, the authors warn that online health information seekers, especially older adults, should always exercise caution as not all information obtained on social media on various health issues, including the coronavirus, is accurate. Besides, consumers of online health information should be thorough and active users of social media;they should use their human senses to discern between false and true health information. Further, they should also verify such information with health professionals if the need arises. Given the limitations enumerated in this study, the authors suggest that further studies are essential to validate the results of this research.
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Mastery of the English language has always been major impediment to many educators and learners around the world. In this rapidly globalising world, social networking sites (SNSs) have risen to the top, especially during the unprecedented advent of the COVID-19 pandemic. Nevertheless, the review of SNSs regarding the English education field is scant, in spite of its significance for sustainability education. On that account, this systematic literature review highlights the potential benefits of using SNSs for educational practices. Using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement, 23 articles from 2018 to 2022 were accessed from three databases, particularly Scopus, Web of Science (WoS), and Educational Resources Information Centre (ERIC). First, the reviewed articles manifest that Facebook is the most frequently used SNSs for English educational purposes. Second, the findings indicated that SNSs have shown a positive impact in enhancing English language competency and improving learning attributes. Third, this review addressed the challenges of using SNSs in the English education field. These findings inferred that there are still research gaps related to the use of SNSs in sustaining English language education. Substantially, this review is advantageous for prospective researchers in identifying the research gaps regarding the use of SNSs for English teaching and learning. © 2023 by the authors.
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OBJECTIVES: To investigate whether sentiment analysis and topic modelling can be used to monitor the sentiment and opinions of junior doctors. DESIGN: Retrospective observational study based on comments on a social media website. SETTING: Every publicly available comment in r/JuniorDoctorsUK on Reddit from 1 January 2018 to 31 December 2021. PARTICIPANTS: 7707 Reddit users who commented in the r/JuniorDoctorsUK subreddit. MAIN OUTCOME MEASURE: Sentiment (scored -1 to +1) of comments compared with results of surveys conducted by the General Medical Council. RESULTS: Average comment sentiment was positive but varied significantly during the study period. Fourteen topics of discussion were identified, each associated with a different pattern of sentiment. The topic with the highest proportion of negative comments was the role of a doctor (38%), and the topic with the most positive sentiment was hospital reviews (72%). CONCLUSION: Some topics discussed in social media are comparable to those queried in traditional questionnaires, whereas other topics are distinctive and offer insight into what themes junior doctors care about. Events during the coronavirus pandemic may explain the sentiment trends in the junior doctor community. Natural language processing shows significant potential in generating insights into junior doctors' opinions and sentiment.
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In modern days, making recommendation for news articles poses a great challenge due to vast amount of online information. However, providing personalized recommendations from news articles, which are the sources of condense textual information is not a trivial task. A recommendation system needs to understand both the textual information of a news article, and the user contexts in terms of long-term and temporary preferences via the user's historic records. Unfortunately, many existing methods do not possess the capability to meet such need. In this work, we propose a neural deep news recommendation model called CupMar, that not only is able to learn the user-profile representation in different contexts, but also is able to leverage the multi-aspects properties of a news article to provide accurate, personalized news recommendations to users. The main components of our CupMar approach include the News Encoder and the User-Profile Encoder. Specifically, the News Encoder uses multiple properties such as news category, knowledge entity, title and body content with advanced neural network layers to derive informative news representation, while the User-Profile Encoder looks through a user's browsed news, infers both of her long-term and recent preference contexts to encode a user representation, and finds the most relevant candidate news for her. We evaluate our CupMar model with extensive experiments on the popular Microsoft News Dataset (MIND), and demonstrate the strong performance of our approach.
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The proceedings contain 27 papers. The topics discussed include: UKP-SQUARE: an online platform for question answering research;ViLMedic: a framework for research at the intersection of vision and language in medical AI;TextPruner: a model pruning toolkit for pre-trained language models;AnnIE: an annotation platform for constructing complete open information extraction benchmark;AdapterHub playground: simple and flexible few-shot learning with adapters;QiuNiu: a Chinese lyrics generation system with passage-level input;automatic gloss dictionary for sign language learners;PromptSource: an integrated development environment and repository for natural language prompts;COVID-19 claim radar: a structured claim extraction and tracking system;TS-Anno: an annotation tool to build, annotate and evaluate text simplification corpora;and CogKGE: a knowledge graph embedding toolkit and benchmark for representing multi-source and heterogeneous knowledge.
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E-commerce has become a booming market for wildlife trafficking, as online platforms are increasingly more accessible and easier to navigate by sellers, while still lacking adequate supervision. Artificial intelligence models, and specifically deep learning, have been emerging as promising tools for the automated analysis and monitoring of digital online content pertaining to wildlife trade. Here, we used and fine-tuned freely available artificial intelligence models (i.e., convolutional neural networks) to understand the potential of these models to identify instances of wildlife trade. We specifically focused on pangolin species, which are among the most trafficked mammals globally and receiving increasing trade attention since the COVID-19 pandemic. Our convolutional neural networks were trained using online images (available from iNaturalist, Flickr and Google) displaying both traded and non-traded pangolin settings. The trained models showed great performances, being able to identify over 90 % of potential instances of pangolin trade in the considered imagery dataset. These instances included the showcasing of pangolins in popular marketplaces (e.g., wet markets and cages), and the displaying of commonly traded pangolin parts and derivates (e.g., scales) online. Nevertheless, not all instances of pangolin trade could be identified by our models (e.g., in images with dark colours and shaded areas), leaving space for further research developments. The methodological developments and results from this exploratory study represent an advancement in the monitoring of online wildlife trade. Complementing our approach with other forms of online data, such as text, would be a way forward to deliver more robust monitoring tools for online trafficking. © 2023 The Author(s)
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The World Wide Web has become an important platform for sharing a wide array of information within the world community. In the post-COVID-19 scenario, the web become a primary source of information in the context of healthcare information dissemination. Healthcare institutions, such as hospitals and clinics, utilize this platform to provide services to reach their target users. It is essential to evaluate the web pages of healthcare institutions and compute their accessibility score for people with disabilities or special needs. This paper presents a variable-magnitude approach to compute the accessibility score of healthcare web pages, considering several requirements of people with disabilities. To compute the accessibility score through the proposed approach, we considered two different components and integrated them to compute the accessibility score through the proposed algorithm. The proposed approach was experimentally applied to sixteen healthcare institutes' web pages in Hungary. Based on the experiment's results and the received feedback from an accessibility specialist, a set of suggestions is provided to minimize the accessibility barrier and improve the accessibility score for people with disabilities to access web resources without difficulty. The main contribution of this work is in enhancing awareness of web platform accessibility for web practitioners to improve accessibility, so that people with disabilities can effectively access web resources. © 2023 by the authors.
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The dominance of social media (SM) channels in disseminating health-related information during the COVID-19 came with several credibility and reliability issues. Yet, users' explicit assessment of SM health-related information (SMHI) quality has been less discussed. Situated in Ghana, this study empirically explores the extent to which the source credibility and content reliability influence users' perception of SMHI quality during the COVID-19 pandemic. Grounded on an integrated SMHI quality evaluation framework, the study hypothesized that the source credibility and content reliability influence users' perception of SMHI quality. We tested the model using 361 Ghanaian SMHI consumers. The structural equation modeling results indicate that while source credibility influences content reliability, it does not influence users' perception of quality SMHI during the COVID-19. Again, content reliability significantly influences users' perceived SMHI quality. We offer several theoretical and practical implications on users' choice and consumption of health-related information on SM platforms.
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The article explores the effectiveness of public health campaigns in Great Britain, highlighting the need to identify the target market for the campaign along with guidance on public perception and wording changes to ensure a negative reaction.
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OBJECTIVE: Migrants and ethnic minority groups have been disproportionately impacted by COVID-19 and have lower levels of vaccine uptake in some contexts. We aimed to determine the extent and nature of social media use in migrant and ethnic minority communities for COVID-19 information, and implications for preventative health measures including vaccination intent and uptake. DESIGN: A systematic review of published and grey literature following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched databases including Embase, Web of Science, PubMed NIH, CINAHL, facilitated through the WHO Global Research on COVID-19 database from 31 December 2019 to 9 June 2021. ELIGIBILITY CRITERIA FOR STUDY SELECTION: Research reporting the use of social media by migrants and/or ethnic minority groups in relation to COVID-19. DATA EXTRACTION: We extracted data on key outcomes, study design, country, population under study and sample size. RESULTS: 1849 unique records were screened, and 21 data sources were included, including populations in the UK, USA, China, Jordan, Qatar and Turkey. We found evidence of consistent use of a range of social media platforms for COVID-19 information in some migrant and ethnic minority populations (including WeChat, Facebook, WhatsApp, Instagram, Twitter, YouTube), which may stem from difficulty in accessing COVID-19 information in their native languages or from trusted sources. Some evidence suggested circulating misinformation and social media use may be associated with lower participation in preventative health measures, including vaccine intent and uptake, findings which are likely relevant to multiple population groups. CONCLUSIONS: Social media platforms are an important source of information about COVID-19 for some migrant and ethnic minority populations. Urgent actions and further research are now needed to better understand effective approaches to tackling circulating misinformation, and to seize on opportunities to better use social media platforms to support public health communication and improve vaccine uptake. REGISTRATION: This study has been registered with PROSPERO (CRD42021259190).
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COVID-19 , Social Media , Transients and Migrants , Humans , Minority Groups , Ethnicity , COVID-19/epidemiology , COVID-19/prevention & control , Ethnic and Racial Minorities , Pandemics/prevention & controlABSTRACT
We describe a study conducted at a large public university campus in the United States which shows the efficacy of network log information for digital contact tracing and prediction of COVID-19 cases. Over the period of January 18, 2021 to May 7, 2021, more than 216 million client-Access-point associations were logged across over 11,000 wireless access points (APs). The association information was used to find potential contacts for approximately 30,000 individuals. Contacts are determined using an AP colocation algorithm, which supposes contact when two individuals connect to the same WiFi AP at approximately the same time. The approach was validated with a truth set of 350 positive COVID-19 cases inferred from the log data by observing associations with APs in isolation residence halls reserved for individuals with a confirmed (clinical) positive COVID-19 test result. The network log data and AP-colocation have a predictive value of greater than 10%;more precisely, the contacts of an individual with a confirmed positive COVID-19 test have greater than a 10% chance of testing positive in the following 7 days (compared with a 0.79% chance if chosen at random, a relative risk ratio of 12.6). A cumulative exposure score is computed to account for exposure to multiple individuals that test positive. Over the duration of the study, the exposure score predicts positive cases with a true positive rate of 16.5% and missed detection rate of 79% at a specified operating point. © 2022 ACM.
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The article mentions the anniversary of the Managing Adult Malnutrition in the Community program to address malnutrition in the United Kingdom (UK) that was launched 10 years ago as of September 2022.
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INTRODUCTION: Heart failure (HF) symptoms improve through self-care, for which adherence remains low among patients despite the provision of education for these behaviours by clinical teams. Open Access Digital Community Promoting Self-Care, Peer Support and Health Literacy (ODYSSEE-vCHAT) combines automated digital counselling with social network support to improve mortality and morbidity, engagement with self-care materials, and health-related quality of life. METHODS AND ANALYSIS: Use of ODYSSEE-vCHAT via Internet-connected personal computer by 162 HF patients will be compared with a control condition over 22 months. The primary outcome is a composite index score of all-cause mortality, all-cause emergency department visits, and HF-related hospitalisation at trial completion. Secondary outcomes include individual components of the composite index, engagement with self-care materials, and patient-reported measures of physical and psychosocial well-being, disease management, health literacy, and substance use. Patients are recruited from tertiary care hospitals in Toronto, Canada and randomised on a 1:1 ratio to both arms of the trial. Online assessments occur at baseline (t=0), months 4, 8 and 12, and trial completion. Ordinal logistic regression analyses and generalised linear models will evaluate primary and secondary outcomes. ETHICS AND DISSEMINATION: The trial has been approved by the research ethics boards at the University Health Network (20-5960), Sunnybrook Hospital (5117), and Mount Sinai Hospital (21-022-E). Informed consent of eligible patients occurs in person or online. Findings will be shared with key stakeholders and the public. Results will allow for the preparation of a Canada-wide phase III trial to evaluate the efficacy of ODYSSEE-vCHAT in improving clinical outcomes and raising the standard of outpatient care. TRIAL REGISTRATION NUMBER: NCT04966104.
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Heart Diseases , Heart Failure , Humans , Quality of Life , Counseling , Social Networking , Randomized Controlled Trials as TopicABSTRACT
Mike Michener, USAID Bureau for Resilience and Food Security The interactive virtual event was held to explore ways of leveraging information technology to build resilient agricultural and food systems in low- and middle-income countries. According to a joint report by the Alliance for Affordable Internet and the World Wide Web Foundation in October last year, 32 low- and middle-income countries missed out on US$1trillion in GDP in the last decade as a result of women’s exclusion in the digital world. The challenge for everyone involved in agricultural development is to ensure that the benefits of the so-called “fourth agricultural revolution” is not limited by lack of access to resources, said Mike Michener, deputy assistant administrator at USAID’s Bureau for Resilience and Food Security, at a fire-side session during the close of the event.
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The article presents the discussion on outreach and remote hospital librarianship. Topics include clinical librarian showing new territory for the author causing moments of pause and concern navigating the most effective outreach methods working from home;and COVID-19 pandemic amplifying the need for virtual services and resources best serving the fluctuating workforce and patron base of hospital libraries.
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In unprecedented times such as the COVID-19 pandemic, many customers are increasingly looking to online platforms and websites to make their purchasing decisions instead of in-store purchases and face-to-face contact with a business representative. Therefore, it is important that businesses, including universities invest the necessary time, effort and resources into their websites to ensure that it is up-to-date, user friendly and regularly used and visited by their customers. To understand better customers' behavioural intention to use university websites, a validated behavioural-intention-to-use scale is necessary. Following a widespread online search of the largest databases available for academic research, no proof of a validated behavioural-intention-to-use scale could be found within the South African university website context. As such, the purpose of this study was to contribute to the literature by explaining the process followed to validate a behavioural-intention-to-use scale within the South African university website context. The study applied a descriptive and single cross-sectional research design. A non-probability convenience sample of 319 Generation Y students completed a self-administered questionnaire. Descriptive statistics, correlation analysis, multicollinearity analysis, reliability and validity measures as well as confirmatory factor analysis (maximum likelihood method) were employed for data analysis. The study's results validate the proposed measurement model of behavioural intention to use university websites as an eight-factor structure that includes information quality, playfulness, system quality, ease of use, trust, attitude, satisfaction and behavioural intention. In addition, the results indicate internal-consistency and composite reliability, nomological, construct, convergent and discriminant validity as well as acceptable model fit.
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Across the country, most professionals caring for children have likely seen an increase in mental illness. Even prior to the COVID‐19 pandemic, there was already a trend of increasing rates of depression and suicidal ideation in American youth (Mojtabai et al., 2016). Since then, the problem has only worsened, as the collective mental health needs of children have been rising rapidly. A combination of factors, from isolation related to school closure and social distancing, to the traumatic effects of losing a parent, have contributed to this dramatic rise in distress (American Academy of Pediatrics, 2021). The CDC reported that the proportion of mental health‐related ED visits increased abruptly after the beginning of the pandemic, with increases of 24% among children aged 5–11 years and 31% among adolescents aged 12–17 years, compared with the same period in 2019 (Leeb et al., 2020). These changes have been widely publicized, and in response to this national crisis, the American Academy of Pediatrics (AAP), American Academy of Child and Adolescent Psychiatry (AACAP), and the Children's Hospital Association (CHA) jointly declared a national state of emergency in October 2021 and created a 10‐point outline for meaningful change in the recognition, prevention, and treatment of childhood mental illness (American Academy of Pediatrics, 2021). In addition, the United States Surgeon General issued an advisory on protecting youth mental health in December 2021 (Department of Health and Human Services, 2021).