Online Researchers Research Articles

Identifying health and healthcare priorities in rural areas: A concept mapping study informed by consumers, health professionals and researchers

BackgroundIt is vital that health service delivery and health interventions address patients’ needs or preferences, are relevant for practice and can be implemented. Involving those who will use or deliver healthcare in priority-setting can lead to health service delivery and research that is more meaningful and impactful. This is particularly crucial in rural communities, where limited resources and disparities in healthcare and health outcomes are often more pronounced. The aim of this study was to determine the health and healthcare priorities in rural communities using a region-wide community engagement approach.MethodsThis multi-methods study was conducted in five rural communities in the Grampians region, Western Victoria, Australia. It involved six concept mapping steps: (1) preparation, (2) generation (brainstorming statements and identifying rating criteria), (3) structuring statements (sorting and rating statements), (4) representation of statements, (5) interpretation of the concept map and (6) utilization. Community forums, surveys and stakeholder consultations with community members and health professionals were used in Step 2. An innovative online group concept mapping platform, involving consumers, health professionals and researchers was used in Step 3.ResultsOverall, 117 community members and 70 health professionals identified 400 health and healthcare issues. Six stakeholder consultation sessions (with 16 community members and 16 health professionals) identified three key values for prioritizing health issues: equal access for equal need, effectiveness and impact (number of people affected). Actionable priorities for healthcare delivery were largely related to access issues, such as the challenges navigating the healthcare system, particularly for people with mental health issues; the lack of sufficient general practitioners and other health providers; the high travel costs; and poor internet coverage often impacting technology-based interventions for people in rural areas.ConclusionsThis study identified actionable health and healthcare priorities from the perspective of healthcare service users and providers in rural communities in Western Victoria. Issues related to access, such as the inequities in healthcare costs, the perceived lack of quality and availability of services, particularly in mental health and disability, were identified as priorities. These insights can guide future research, policy-making and resource allocation efforts to improve healthcare access, quality and equity in rural communities.

Inclusion, Diversity, Equity and Access: Online Teaching Strategies and Institutional Support

ABSTRACT Research on online teaching and learning has increased however there is still a need for research to focus on inclusion, diversity, equity and access in online teaching and learning. This qualitative case study aims to identify inclusion, diversity, equity and access strategies for design, facilitation, assessment and of online courses. In addition, it also examines the institutional support and policies that currently exist or are needed for online instruction. Twenty one instructors who taught online at a southeastern university were interviewed. Through two rounds of coding and categorizing, the research team identified several themes. For design and facilitation, the emerging IDEA themes included freedom and autonomy, diversity as a course topic, diverse perspectives, flexibility, and building relationships. For assessment and evaluation, the themes that emerged included assessment design, learner participation, soliciting student feedback, evaluation criteria, and instructor commitment. For institutional support and policies, themes that emerged included need for integrated support, and the need for policies and evaluation criteria. The findings from this study have implications for online instructors, instructional designers, administrators, students, and researchers to integrate inclusion, diversity, equity and access effectively in online courses.

THE INFLUENCE OF LEARNERS’ PERCEPTION OF READING DIFFICULTIES AND THEIR ONLINE READING STRATEGIES

In recent years, the advent of digital technology has significantly transformed the landscape of reading, giving rise to a distinct set of skills and strategies required for effective comprehension in online environments. The transition to online reading has prompted the need for individuals to adapt and develop specific strategies to navigate digital texts successfully. This study is conducted to explore the influence of learners' perception of reading difficulties and their online reading strategies. This quantitative study collected data from 124 respondents from various faculties of a public university. The questionnaire has 4 sections that contain items on demographic profile, reading difficulties, global strategies, problem-solving strategies, and support strategies. Generally, the finding revealed that there is no correlation between learners’ perception of reading difficulties and their online reading strategies. By understanding the influence of learners' perceptions of reading difficulties and their online reading strategies, educators and researchers can work together to optimise the use of digital technologies in education and improve learning outcomes for students.

“Stuff that only mixed‐race people would understand”: Community and identity‐related experiences in online groups for multiracial people

AbstractMultiracial people are a rapidly growing group who may still lack social support from similar others in their offline lives. This study aimed to understand the user experiences in online groups for multiracial individuals. In a cross‐sectional online survey, 300 multiracial emerging adults (79% women; mean age = 23; most common ethnic identity components: 86% White/European, 44% Chinese, 17% Japanese, 16% Southeast Asian, and 13% Filipino) answered open‐ended questions about their experiences in Facebook groups for multiracial people. Participants described aspects of the groups they liked and disliked most, as well as their identity‐related experiences in the groups. We used reflexive thematic analysis to generate four themes within participants responses: (1) online groups as a gateway into connections with other multiracial individuals, (2) deriving a sense of belonging and community, (3) an opportunity for exploration and validation of racial identity, and (4) negative intrusions such as negative content (e.g., trolling, repetitive posting) and racism. For racial and ethnic minorities such as multiracial individuals, Facebook groups provide a space for community‐building and identity‐related experiences. Although online groups can foster a strong sense of community, validation, and belonging, findings also highlight potential challenges and limitations (e.g., how to delineate group boundaries, difficulty forming close relationships). Online group users and researchers should explore novel ways of maximizing these positive experiences while addressing users' concerns and negative experiences.

Do You Consent to the Use of Your Biological Data for Training ML and AI Models? Online Survey Targeting Clinicians and Researchers.

Aim: The majority of machine learning (ML) models in healthcare are built on retrospective data, much of which is collected without explicit patient consent for use in artificial intelligence (AI) and ML applications. The primary aim of this study was to evaluate whether clinicians and scientific researchers themselves consent to provide their own data for the training of ML models. Materials and Methods: This survey was conducted through an anonymous online survey, utilizing platforms such as Telegram, LinkedIn, and Viber. The target audience comprised specific international groups, primarily Russian, German, and English-speaking, of clinicians and scientific researchers. These participants ranged in their levels of expertise and experience, from beginners to veterans. The survey centered on a singular, pivotal question: “Do You Consent to the Use of Your Biological and Private Data for Training Machine Learning and AI Models?” Respondents had the option to choose from three responses: “Yes” and “No”. Results: The survey was conducted in January 2024. A total of 119 unique and verified individuals participated in the survey. The results revealed that only 50% of respondents (63 persons) expressed consent to provide their own data for the training of ML and AI models. Conclusion: In the development of ML and AI models, particularly open-source ones, it is crucial to ascertain whether participants are willing to provide their private data. While ML algorithms can transform the nature of data, it is important to remember that the primary owner of this data is the individual. Our findings show that in 50% of the cases, even participants from scientific research and clinical backgrounds – individuals typically accountable for ensuring data quality in AI and ML model development – do not consent to the use of their data in AI and ML settings. This highlights the need for more stringent consent processes and ethical considerations in the utilization of personal data in AI and ML research.

Using machine learning to understand driving behavior patterns

Driver behavior is one of the principal factors associated with road accidents. Much research to date focusing on Machine learning technology has been successfully applied to identifying driving styles and recognizing unsafe behaviors. In this paper, the development of an android mobile application (Driver Alert) is described, with the aim of collecting data from mobile phone sensors data to identify certain patterns and understand drivers’ behaviors. Additional information was recorded regarding weather and traffic information, using public API’s to complement the data directly collected from the vehicle. Four machine learning models (K-Means, Algorithm Agglomerative Hierarchical, Random Forest and Support Vector Machines) were tested and compared to identify different driver profiles. A native mobile application named DriverAlert was developed to support collect data and make it available, through an online dashboard, to drivers and researchers. Due to the available tools and libraries, it possesses, Python language was used, as it is a powerful programming language for workloads in data science, machine learning, and scientific computing.

Digitalization of the Medical System and Demographic Processes in Estonia

The rapid growth in information technologies has produced effects for all the walks of human and public life. The health care sector did not stay out, although it needs to be acknowledged that it has not been effectively digitalized in all the countries, even developed ones. It should be emphasized that the demographic situation heavily depends on the health care system performance. A most advanced country in terms of the health care digitalization is certainly Estonia. This country with less than 1,500,000 of population has long digitalized all the services provided to its residents. 
 The article’s objective is to investigate the impact of health care system digitalization on demographic processes in Estonia. Some innovations in the Estonian medial system, implemented with the advent of new information technologies, are discussed: for doctors it is possible to exchange paperless information about a patient in the patient’s presence in case of need (including medical records or results of tests and examinations). On the other hand, such confidential information can be closed by the patient’s desire for any medical employee. Moreover, a person is well aware of who exactly has access to his/her data: doctor, police or an official body. 
 Being a digital country, Estonia was better prepared to deal online with various problems (first and foremost, distance training, distance job and medical service: testing, identifying contacts with ill persons and vaccination later on) posed by the pandemic of COVID-19. However, a change in the demographic situation, resulting from the spread of the virus, was triggered e. g. by the health care system performance and its response on the new challenge.
 It is emphasized that important information sources for investigating the demographic situation in Estonia and the health care system’s impact on demographic change include the statistical office and other official bodies, as well as international institutions. Using information on the pandemic extracted from online registers, researchers, medics, statisticians and professionals in other fields could help heath care system in quicker adaptation to new realities and in implementing additional methods for analysis of the morbidity rate in Estonia. 
 The experience of the Estonian health care system in implementing digitalization is undoubtedly important for Ukraine. It refers to creating new registers and systems already used in Estonia and other North European countries but lacking in Ukraine, and to improving existing medical information systems and their interactions.

The i-SUN process to use social learning analytics: a conceptual framework to research online learning interaction supported by social presence

Interaction is an essential element of online learning and researchers had use Social Learning Analytics (SLA) to understand the characteristics of meaningful interaction. While the potential for network analysis in education (i.e., SLA) is valuable, limited research has considered how best to use this emerging field to inform meaningful interaction in online settings. Online learning researchers need a concise and simplified framework for SLA to support interaction in online learning environments. Therefore, we present a conceptual framework to make SLA accessible for researchers investigating learners' interactions in online learning. The framework includes concepts from network theory and the online learning literature integrated into a new perspective to analyze learners' online behaviors and interactions. We analyzed existing models and frameworks to show how network analysis has been used in online learning resulting in a conceptual environment to investigate learner interaction. The proposed i-SUN framework has four main steps: (1) interaction, (2) social presence alignment, (3) unit of analysis definition, and (4) network statistics and inferential analysis selection. We also identified five ways in which the i-SUN model contributes to the advancement of SLA in online interaction research and provide recommendations for empirical validation. As part of a sequence of manuscripts, we seek to offer a unique perspective to online learning researchers and practitioners by focusing on the social and pedagogical implications of applying network analysis to understand online learning interaction.

What kind of presence? The issue of presence of the people in a Live Streaming Eucharist in the Diocese Bandung, Indonesia

ABSTRACT A survey-based quantitative research project in the Catholic Diocese of Bandung, Indonesia during the Covid-19 pandemic and its subsequent lockdown is presented in this article. The Diocese instigated a temporary online Live Streaming Eucharist and researchers surveyed 771 respondents about their participation and experience. The data collected raises a specific and particular question about what it means for Catholic Christians to be present at a Eucharist – both online, virtually and offline, in person. This article elaborates not only on the question of real or virtual participation but also on the presence of the people as a communion: how they are present. The importance of active participation, how people dress, how people give money and how they choose which online worship all need to be accounted for when developing a nuanced understanding of presence at the Eucharist.

COVID-19 Academic Integrity Violations and Trends: A Rapid Review

The rapid shift from classroom course delivery to online education modalities during the COVID-19 pandemic has had significant impacts on academia. Student loss of face-to-face interaction, the lost social benefits of the educational milieu, and restricted instructor ability to control both the learning environment and assessment process have been significant. The purpose of this paper is to discover if due to the unplanned shift to online course delivery, educators and researchers experienced impacts to academic integrity during the peak of the online shift. A systemic review utilizing the PRISMA methodology of peer reviewed literature published during the period of March 2020 till September 2021 demonstrated that violation types continued to fall within the existing academic integrity constructs of inappropriate information sharing, cheating on exams and assignments, incidents of plagiarism, and falsifying or fabricating information. The results showed that pre-COVID concerns with academic integrity were amplified with previous concerns moving to the forefront. In addition, the rapid shift opened doors for greater opportunity for violations and increased instructor concern especially within the hard sciences and courses with lab-based components. Reinforcing the importance of providing formal academic integrity student and faculty training can be a beneficial intervention to ensure students understand the ethical implications of student behavior and performance during the assessment process. Given the emerging trend pre-COVID that skyrocketed during the pandemic, ensuring academic integrity should remain a key priority for learning institutions.

Empirical Study on Electronic Learning System: Benefits, Challenges and Prospects

The swift growth of digital technology has led to the adoption of emerging technologies in all human endeavours. Over the years, the rate of smartphone users has increased rapidly which makes so many developed nations adopt online learning as a means of taking education to everyone irrespective of their geographical location. Similarly, the COVID-19 outbreak makes so many developing countries adopt the electronic learning system as a viable approach to teaching and learning. Consequently, the e-learning system is useful for the creation of informative online sessions, supplementary lessons for students, webinars, e-mentoring and online conferences. This paper uses an empirical approach to evaluate the benefits and challenges associated with the e-learning system, and a survey was conducted among students in a higher institution in Nigeria to know the likely challenges faced by students with the e-learning system and also at different online researchers forum across the globe. A total of four hundred and ninety-five (495) responses were gathered and the data was analysed using descriptive statistics. The result shows that 33.94% of students complained about weak internet connectivity from the questionnaire administered which is also a major problem in most developing nations, while only 7.27% of respondents complained about limited feedback, it is recommended that there should be a provision of standard IT infrastructure, strong internet connectivity, and user-friendly e-learning platform, its implementation will improve performance and lead to greater productivity.

Phone and Self: How Smartphone Use Increases Preference for Uniqueness

One of the most dramatic shifts in recent years has been consumers’ increased use of smartphones for making purchases and choices—but does using a smartphone influence what consumers choose? This article shows that, compared with using a personal computer (PC), making choices using a personal smartphone leads consumers to prefer more unique options. The authors theorize that because smartphones are considerably more personal and private than PCs, using them activates intimate self-knowledge and increases private self-focus, shifting attention toward individuating personal preferences, feelings, and inner states. Consequently, making choices using a personal smartphone, compared with a PC, tends to increase the preference for unique and self-expressive options. Six experiments and several replications examine the effects of personal smartphone use on the preference for unique options and test the underlying role of private self-focus. The findings have important implications for theories of self-focus, uniqueness seeking, and technology’s impact on consumers, as well as tangible implications for many online vendors, brands, and researchers who use mobile devices to interact with their respective audiences.

Extracting Actionable Insights from Text Data: A Stable Topic Model Approach

Topic models are becoming a frequently employed tool in the empirical methods repertoire of information systems and management scholars. Given textual corpora, such as consumer reviews and online discussion forums, researchers and business practitioners often use topic modeling to either explore data in an unsupervised fashion or generate variables of interest for subsequent econometric analysis. However, one important concern stems from the fact that topic models can be notorious for their instability, i.e., the generated results could be inconsistent and irreproducible at different times, even on the same dataset. Therefore, researchers might arrive at potentially unreliable results regarding the theoretical relationships that they are testing or developing. In this paper, we attempt to highlight this problem and suggest a potential approach to addressing it. First, we empirically define and evaluate the stability problem of topic models using four textual datasets. Next, to alleviate the problem and with the goal of extracting actionable insights from textual data, we propose a new method, Stable LDA, which incorporates topical word clusters into the topic model to steer the model inference toward consistent results. We show that the proposed Stable LDA approach can significantly improve model stability while maintaining or even improving the topic model quality. Further, employing two case studies related to an online knowledge community and online consumer reviews, we demonstrate that the variables generated from Stable LDA can lead to more consistent estimations in econometric analyses. We believe that our work can further enhance management scholars’ collective toolkit to analyze ever-growing textual data.

All That’s Happening behind the Scenes: Putting the Spotlight on Volunteer Moderator Labor in Reddit

Online volunteers are an uncompensated yet valuable labor force for many social platforms. For example, volunteer content moderators perform a vast amount of labor to maintain online communities. However, as social platforms like Reddit favor revenue generation and user engagement, moderators are under-supported to manage the expansion of online communities. To preserve these online communities, developers and researchers of social platforms must account for and support as much of this labor as possible. In this paper, we quantitatively characterize the publicly visible and invisible actions taken by moderators on Reddit, using a unique dataset of private moderator logs for 126 subreddits and over 900 moderators. Our analysis of this dataset reveals the heterogeneity of moderation work across both communities and moderators. Moreover, we find that analyzing only visible work – the dominant way that moderation work has been studied thus far – drastically underestimates the amount of human moderation labor on a subreddit. We discuss the implications of our results on content moderation research and social platforms.

How to Deeply Analyze the Content of Online Newspapers Using Clustering and Correlation

The increase in the number of visitors is one of the keys to increasing income for online newspapers, whether to increase the number of ads, Google AdSense, or customer trust. Therefore, finding which news categories increase the number of visitors needs to be known and analyzed more deeply. Because it is very common to add content to online newspaper sites every day, even for hours, this pattern analysis is not the same as analyzing regular website content patterns. This study intends to add methods in the world of research on how to analyze website content, especially online news, by using the clustering method to classify what news categories bring high, medium, or a low number of visitors and then analyzing the correlation to explore the depth of the relationship between the variables, namely which parameters have a large or low effect on the increase in the number of visitors. A local Batam-based online newspaper company is used as a case study for this research. Data is collected, preprocessed first, and analyzed using the clustering and correlation method. This analysis of the news content readership suggests what news categories should be optimized because it provides an increase in the number of visitors. A summary of the analysis steps in this study is presented. We also provided some suggestions if other online newspaper owners or researchers are interested in a similar analysis of online news content.

Constructing radical community: an ecological model for shifting from an EdD to a We-dD in online doctoral programs.

Online learning is often associated with the alone together paradox that suggests that online students are constantly connected to one another yet feel more alone due to a lack of real social connection. While research has approached this issue from an environmental perspective, some scholars have recently suggested that aspects of online students’ complex ecologies, beyond the online program, may also influence the degree students engage socially. This phenomenological study explores the experiences of 10 students enrolled in an online doctoral program in education. In-depth interviews were conducted to discuss how various ecological systems affect students’ ability to socially connect in the online program, revealing students reporting feeling apart, but together, rather than alone together. This study encourages online doctoral programs and researchers to take an ecological approach, as opposed to an environmental one, in order to construct a more thorough understanding of the online doctoral student experience.

Considerations for Future Online Testing and Assessment in Colleges and Universities

AbstractThe onset of the coronavirus pandemic forced schools and universities across the nation and world to close and move to distance learning rather immediately. Almost two years later, colleges and universities have reopened, and most students have returned to campuses, but distance learning still occurs at a much higher rate than before the beginning of the pandemic. A few preliminary studies have shown that online assessment results are similar to in‐person assessment results. However, as colleges and universities have expanded their online options, new issues have arisen related to best practices, equity, fairness, test security, and test integrity. Paper‐and‐pencil assessments should not merely be moved to online assessments without careful consideration to how this administration change affects all students. With the switch to more online assessments, educators and researchers should thoroughly evaluate how to provide these assessments in the most secure, fair, and valid manner while also maintaining the test or assessment's integrity and interpretation.

Conducting product comparative analysis to outperform competitor’s product using Teardown JST Model

Due to the massive number of products being produced every year in every industry, firms have witnessed a tremendous growth in innovation of methods to create a sustainable competitive advantage. For the past decade and with the availability of online consumer reviews, companies and researchers have developed many approaches utilizing electronic Word-of-Mouth to improve and develop products and services to outperform competitors. The purpose of this study is to construct an effective method to perform a better product comparative analysis based on online consumer reviews. We propose a novel framework called Teardown Joint Sentiment-Topic analysis model consisting of a combination of text analytical approaches incorporated with a developed method of the traditional teardown analysis product comparative approach. The proposed approach is fully unsupervised model that employs Latent Dirichlet Allocation topic modeling to form topics which are classified according to their sentiments. Topics are then analyzed against competitive products and critical topics are identified using a developed teardown method. A case study analyzing online customer reviews of competing products in two domains (i.e., mobile phones and surveillance cameras) is conducted. The identified critical topics are further analyzed in view of products’ specifications perspective. We found that the detected aspects of the selected products are indeed critical, and hence, they need to be improved in order to gain a competitive advantage. The significant result of this study shows that the proposed method is effective in conducting products comparative analysis and provides valuable insights into utilizing the consumer reviews for product development.

Transnational online research: recognising multiple contexts in Skype-to-phone interviews

This paper lies at the intersection of discussions surrounding digitally mediated research methods and transnational research projects. It contributes to the current methodological debate surrounding online interviewing by focusing on tensions and affordances involved in Skype-to-phone interviewing in a transnational research context. While the Skype-to-phone facility does indeed increase further access to global participants, complex power hierarchies and ethical concerns continue to exist in relation to technological access/infrastructure, research governance regimes in different places and interpersonal research relations. We, therefore, propose that online researchers involved in transnational research projects using Skype methods move towards consideration of multiple competing constituencies and diverse social and spatial connectivities and power hierarchies in which they are researching. These social differences and spatial registers are not swept away through research conducted in a uniform virtual digital environment; rather transnational researchers must make explicit the multiple place-based contexts of their digitally mediated research, as they shape the research process in distinct ways. Thus, specific consideration must be given to ethical concerns that emanate from transnational online research.

DeepPick: A Deep Learning Approach to Unveil Outstanding Users Ranking with Public Attainable Features

Outstanding users (OUs) denote the influential, "core" or "bridge" users in the online community. How to accurately detect and rank them is an important problem for third-party online service providers and researchers. Conventional efforts, ranging from early graph-based algorithms to recent machine learning-based approaches, typically rely on an entire network's information or at least ego networks. However, for privacy-conscious users or newly-registered users, such information is not easily accessible. To address this issue, we present DeepPick, a novel framework that considers both the generalization and specialization in the detection task of OUs. For generalization, we introduce deep neural networks to capture nonlinear features. For specialization, we leverage the traditional well-defined metrics to preserve common features. Extensive experiments based on real-world datasets demonstrate that our approach achieves a high efficacy in terms of detection performance against the state-of-the-art.