METHODOLOGICAL AND ETHICAL ISSUES OF RESEARCH WITH OLDER ADULTS IN PUERTO RICO DURING COVID-19

Engaging diverse groups of older adults is essential to addressing health disparities. In this presentation, we will describe how we used our research team's networks and knowledge of the target population to engage key stakeholders in our KAP project. We will address our partnerships with clergy, NGO staff and administrators, housing authorities, colleagues at Puerto Rican universities, and older adults who serve as informal community and institutional gatekeepers. We will discuss how these partnerships facilitated our ability to identify and access study sites and participants;conduct telephone and in-person interviews;ensure data quality through training and monitoring;and, assure safety through adherence to COVID-19 protocols. Finally, we will describe key cultural and ethical issues of conducting research with older adults through community partnerships during a pandemic. The presentation has implications for developing beneficial partnerships with local community leaders and enhancing the representation of diverse groups of older adults in research.

Detection of False Information in Spanish Using Machine Learning Techniques

This research focuses on the detection of false claims in Spanish through the use of machine learning techniques. Most of the current work related to automated, or semi-automated, fake news detections are carried out for the English language, however, there is still a large room for improvement in other languages such as Spanish. The detection of fake news content and its dissemination (spread) in online platforms is an open and hard problem, this work is focused on the detection of false and misleading information spreading during the election campaign in the Spanish Parliament and Catalonia crisis in 2019, migration crisis, COVID-19 pandemic, and hate speech against minorities. We propose the use of a machine learning model adapted for dealing with human language understanding tasks, called BERT, which has been trained and experimentally tested. We have collected a corpus of different types of false information and claims such as articles, posts on Facebook, WhatsApp's messages, tweets, and others. The results evidence how usage of machine learning techniques can help in the identification of false statements with more than 88% accuracy, and in collecting samples of false information. The experiments, with a comparison between different machine learning methods, have also been carried out using previous datasets, providing a comparison between different approaches. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Disinformation and vaccines on social networks: Behavior of hoaxes on Twitter

Introduction: Anti-vaccine disinformation is highly dangerous due to its direct effects on society. Although there is relevant research on typologies of hoaxes, denialist discourses on networks or the popularity of vaccines, this study provides a complementary and pioneering vision about the anti-vaccine discourse of COVID-19 on Twitter, focused on its spreaders’ behavior. Methodology: Given an initial sample of a hundred hoaxes (from December 2020 to September 2021) for the download of 200,246 tweets, around 36,000 tweets (N=36.292) that support or deny disinformation have been filtered through an algorithm for Natural Language Inference (NLI) to analyze their spreaders’ through their metrics in the platform. Results: In relative numbers, the results show, among others, more hoaxes with original content (not retweets) among accounts with more followers and those verified;more irruption of disinformation opposed to its objection by accounts created between 2013 and 2020, and the association of the acknowledgement (more presence in lists or many more followers than followed users) to the preference for denying false information instead of approving it. Discussion: The article shows how the typology of the accounts can be a predictive factor about the behavior of users who spread disinformation. Conclusions: Similar behavioral patterns of anti-vaccine discourse are revealed according to the accounts’ Twitter-related indicators. The size of the sample and the techniques used give a solid foundation for other comparative studies on disinformation about health and on other phenomena on social networks. © 2023, University of La Laguna. All rights reserved.

Nursing in epidemiological surveillance systems and a perspective from university teaching

This descriptive essay highlights the roles and competencies of the nursing professional in the surveillance, prevention, and infection control systems in a Covid-19 pandemic situation;and a perspective from university teaching is proposed for the training of professionals committed to public health. Infectious contagious events with pandemic potential alter the life dynamics of the groups, generating problems of the following type: social, political, economic, cultural and health;Consequently, the nursing professional has a responsibility of great social value, which implies a praxis based on scientific evidence, leadership, commitment, creativity, proactivity, and assertive and empathetic communication with the needs of the groups. in a pandemic situation.

Countering Misinformation Through Semantic-Aware Multilingual Models

The presence of misinformation and harmful content on social networks is an emerging problem that endangers public health. One of the most successful approaches for detecting, assessing, and providing prompt responses to this misinformation problem is Natural Language Processing (NLP) techniques based on semantic similarity. However, language constitutes one of the most significant barriers to address, denoting the need to develop multilingual tools for an effective fight against misinformation. This paper presents an approach for countering misinformation through a semantic-aware multilingual architecture. Due to the specificity of the task addressed, which involves assessing the level of similarity between a pair of texts in a multilingual scenario, we built an extension of the well-known Semantic Textual Similarity Benchmark (STSb) to 15 languages. This new dataset allows to fine-tune and evaluate multilingual models based on Transformers with a siamese network topology on monolingual and cross-lingual Semantic Textual Similarity (STS) tasks, achieving a maximum average Spearman correlation coefficient of 83.60%. We validate our proposal using the Covid-19 MLIA @ Eval Multilingual Semantic Search Task. The results reported demonstrate that semantic-aware multilingual architectures are successful at measuring the degree of similarity between pairs of texts, while broadening our understanding of the multilingual capabilities of this type of models. The results and the new multilingual STS Benchmark data presented and made publicly in this study constitute an initial step towards extending methods proposed in the literature that employ semantic similarity to combat misinformation at a multilingual level. © 2021, Springer Nature Switzerland AG.

YoungRes: a serious game-based intervention to increase youngsters resilience against extremist ideologies

Extremist ideologies are proliferating nowadays in both political and social levels. Considering that youngsters are in a development stage where they are still conforming their own social identity, they become especially vulnerable to these ideologies’influence. Therefore, it becomes critical to provide them with the psychological skills to rationalize and resist those influences. Video games, which are already a technology commonly consumed by these generations, provide a way to motivate and engage youngsters. Therefore, implementing these video games in interventions to increase psychological resilience represents an opportunity to create an innovative learning approach. Following this motivation, this paper has three main objectives: adapting a traditional emotional intelligence training program to a novel serious game based intervention, called YoungRes;providing a metric to measure the student’evolution based on in-game behavioural patterns, instead of indirect measures;and evaluating the impact of the intervention itself after its implementation. To do so, an 11 sessions intervention was applied to 36 students from two primary schools in Spain. Quantitative and qualitative data was extracted from the experience, consisting on data extracted from the player’s behaviour and a final survey. A detailed statistical analysis carried out showed two main outcomes: first, the serious game based intervention was very appreciated by the students, specially by those who frequently play video games;second, the intervention allowed to improve several emotional intelligence competences, such as active listening and controlled breathing, as well as to promote knowledge about the Islamic culture. Finally, the authors discussed about how the game could be improved for future applications in schools. Author

Flegmasia cerúlea dolens and venous gangrena associated with infection by SARS-CoV-2: Case report and literature review

Cerulean phlegmasy dolens (CDF) and venous gangrene are the most serious manifestations of acute deep vein thrombosis (DVT). We present the case of a 64-year-old woman who was admitted to the emergency service for pain in the left lower limb and localized edema in the foot with a diagnosis of venous gangrene after the corresponding clinical, laboratory and imaging analysis. Given the torpid evolution, fasciotomies were performed with evident improvement in the picture. When filing the cause of this event, it is attributed to the infection by SARS-CoV-2 as the trigger for this venous gangrene. © 2021 SEACV.

Impact on clinical practice of the preoperative screening of Covid-19 infection in surgical oncological patients. Prospective cohort study.

BACKGROUND: In the oncological patient, an COVID-19-Infection, whether symptomatic or asymptomatic, a surgical procedure may carry a higher postoperative morbidity and mortality. The aim of this study was to describe the impact on clinical practice of sequential preoperative screening for COVID-19-infection in deciding whether to proceed or postpone surgery. METHODS: Prospective, cohort study, based on consecutive patients' candidates for an oncological surgical intervention. Sequential preoperative screening for COVID-19-infection: two-time medical history (telematic and face-to-face), PCR and chest CT, 48 h before of surgical intervention. COVID-19-infection was considered positive if the patient had a suggestive medical history and/or PCR-positive and/or CT of pneumonia. RESULTS: Between April 15th and May 4th, 2020, 179 patients were studied, 97 were male (54%), mean (sd) age 66.7 (13,6). Sequential preoperative screening was performed within 48 h before to surgical intervention. The prevalence of preoperative COVID-19-infection was 4.5%, 95%CI:2.3-8.6% (8 patients). Of the operated patients (171), all had a negative medical history, PCR and chest CT. The complications was 14.8% (I-II) and 2.5% (III-IV). There was no mortality. The hospital stay was 3.1 (sd 2.7) days.In the 8 patients with COVID-19-infection, the medical history was suggestive in all of them, 7 presented PCR-positive and 5 had a chest CT suggestive of pneumonia. The surgical intervention was postponed between 15 and 21 days. CONCLUSION: Preoperative screening for COVID-19-infection using medical history and PCR helped the surgeon to decide whether to go ahead or postpone surgery in oncological patients. The chest CT may be useful in unclear cases.