A Quantitative Analysis of Barriers to International Telemedicine Education: The Case of the Philippines

A multitude of studies have investigated the opportunities and limitations of telemedicine pre- and post-COVID-19 pandemic. However, most of the research has focused on telemedicine's constraints in the context of international, regional, and developed nations, with few studies examining the specific challenges that may affect telemedicine's progress in developing countries where the pandemic may have exacerbated existing technological and geographical difficulties. This study takes the Philippines as a case study due to its archipelagic location, use of English as an official language, and other factors that influence its adaptability to international telemedicine. We assessed the barriers and challenges to the advancement of telemedicine from four viewpoints: policy, organization, individual, and collaboration between organizations. Therefore, the significance of this study is twofold: (a) to concentrate on international telemedicine education by contrasting domestic and international practices, and (b) to newly reveal connections between each component, as prior research highlighted barriers and difficulties but did not clarify relationships among different elements. We surveyed and interviewed 38 physicians, technicians, coordinators, and staff involved in telemedicine education in the Philippines. The study found that (1) public support yields favourable results, (2) a strong correlation exists between domestic and international telemedicine, (3) communication and technical obstacles are interconnected, (4) unity and cooperation in intra-hospital collaboration are critical, and (5) comprehending the "significance of work" has a positive impact. This study underscores the intersectionality of several barriers to telemedicine development. It also recommends providing greater support for telemedicine education in developing nations and promoting collaboration between developing and developed nations. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

Changing Post-Adoptive Personal Healthcare IT Use during the COVID-19 Pandemic

We examine post-adoptive IT use of fitness tracking technologies longitudinally using three data sets gathered before, during, and after the COVID-19 lockdowns in the United States. Using adaptive structuration theory (AST) as a meta-theory, we model post-adoptive IT use as having two fundamental types (continued and novel), each having distinct psychological and sociological antecedents. Sociological antecedents are further broken down into those coming from society and those coming from the technology. Findings indicate there are strong correlations between antecedents and the two types of use in all three data sets. Post-hoc analysis indicates continued and novel use vary across time. These variations are not static and appear to be non-linear. Implications and future research directions are also discussed. © 2022 IEEE Computer Society. All rights reserved.

Analysis and Incidents in the Professional Practices of IT University Students in the Context of the COVID-19 Pandemic

The objective of this research is to analyze the level of satisfaction and effectiveness of pre-professional practices in university students, before and during the pandemic. The analyzed data was collected through surveys applied to 67 students. The data was analyzed with a descriptive approach, using tables to summarize the results, while in the analysis of the difference in means in the effectiveness and satisfaction scales, the ANOVA method was used, obtaining a p- value= 0.1134, the same as indicated that there is no variation in the means of the scales. For the correlation analysis, the Pearson coefficient was calculated, whose value indicated a strong correlation between the satisfaction scale of the place where the practice was carried out and the level of satisfaction (0.637). Finally, a web page model is proposed that is capable of better guiding the choice of the place where the students will carry out the pre-professional practices. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The Impact of Tweets, Mandates, Hesitancy and Partisanship on Vaccination Rates

This study aims to carry out a cross sectional study of how a state’s majority political affiliation, vaccine hesitancy and exposure to misinformed news on social media affects overall vaccination rates. The target country in this study is the United States of America and the granularity of data used is on a state level. Analysis is conducted using a fusion of information from various data sources such as CDC, US Census, Twitter API. Specifically, vaccination rates are taken from CDC, hesitancy rates from US Census, the state’s political affiliation from past elections, and social media misinformation from twitter feeds. The three main study research findings are summarised as follows. First, the findings show the strong correlation between political party affiliation and vaccination rates/vaccine hesitancy rates. As a corollary, there is also a strong negative correlation between vaccination hesitancy and vaccination rates. Second state-wide vaccination mandates typically increased the vaccination rates. Interestingly, states without a state-wide mandate had similar vaccination/vaccine hesitancy rates as states who had banned the state-wide mandates. Finally, the paper shows there will always be a baseline of misinformation in social media articles. However when either (1) the average retweet or (2) the average number of followers reached per tweet crosses a certain critical mass, it will adversely affect the vaccination rate of a state. This suggests that eradicating all misinformation from social media will be counterproductive, rather it is more critical to curtail misinformation before they reach a critical mass. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

POLE: Polarized embedding for signed networks

From the 2016 U.S. presidential election to the 2021 Capitol riots to the spread of misinformation related to COVID-19, many have blamed social media for today's deeply divided society. Recent advances in machine learning for signed networks hold the promise to guide small interventions with the goal of reducing polarization in social media. However, existing models are especially ineffective in predicting conflicts (or negative links) among users. This is due to a strong correlation between link signs and the network structure, where negative links between polarized communities are too sparse to be predicted even by state-of-the-art approaches. To address this problem, we first design a partition-agnostic polarization measure for signed graphs based on the signed random-walk and show that many real-world graphs are highly polarized. Then, we propose POLE (POLarized Embedding for signed networks), a signed embedding method for polarized graphs that captures both topological and signed similarities jointly via signed autocovariance. Through extensive experiments, we show that POLE significantly outperforms state-of-the-art methods in signed link prediction, particularly for negative links with gains of up to one order of magnitude. © 2022 Owner/Author.

Online Partisan Polarization of COVID-19

In today's age of (mis)information, many people utilize various social media platforms in an attempt to shape public opinion on several important issues, including elections and the COVID-19 pandemic. These two topics have recently become intertwined given the importance of complying with public health measures related to COVID-19 and politicians' management of the pandemic. Motivated by this, we study the partisan polarization of COVID-19 discussions on social media. We propose and utilize a novel measure of partisan polarization to analyze more than 380 million posts from Twitter and Parler around the 2020 US presidential election. We find strong correlation between peaks in polarization and polarizing events, such as the January 6th Capitol Hill riot. We further classify each post into key COVID-19 issues of lockdown, masks, vaccines, as well as miscellaneous, to investigate both the volume and polarization on these topics and how they vary through time. Parler includes more negative discussions around lockdown and masks, as expected, but not much around vaccines. We also observe more balanced discussions on Twitter and a general disconnect between the discussions on Parler and Twitter. © 2021 IEEE.

Multi-modal Information Fusion-powered Regional Covid-19 Epidemic Forecasting

With the current raging spread of the COVID19, early forecasting of the future epidemic trend is of great significance to public health security. The COVID-19 is virulent and spreads widely. An outbreak in one region often triggers the spread of others, and regions with relatively close association would show a strong correlation in the spread of the epidemic. In the real world, many factors affect the spread of the outbreak between regions. These factors exist in the form of multimodal data, such as the time-series data of the epidemic, the geographic relationship, and the strength of social contacts between regions. However, most of the current work only uses historical epidemic data or single-modal geographic location data to forecast the spread of the epidemic, ignoring the correlation and complementarity in multi-modal data and its impact on the disease spread between regions. In this paper, we propose a Multimodal InformatioN fusion COVID-19 Epidemic forecasting model (MINE). It fuses inter-regional and intra-regional multi-modal information to capture the temporal and spatial relevance of the COVID-19 spread in different regions. Extensive experimental results show that the proposed method achieves the best results compared to state-of-art methods on benchmark datasets. © 2021 IEEE.