Discussions About COVID-19 Vaccination on Twitter in Turkey: Sentiment Analysis

Objectives: The present study aims to examine COVID-19 vaccination discussions on Twitter in Turkey and conduct sentiment analysis. Methods: The current study performed sentiment analysis of Twitter data with artificial intelligence (AI)'s Natural Language Processing (NLP) method. The tweets were retrieved retrospectively from March 10, 2020, when the first Covid-19 case was seen in Turkey, to April 18, 2022. 10308 tweets accessed. The data were filtered before analysis due to excessive noise. First, the text is tokenized. Many steps were applied in normalizing texts. Tweets about the COVID-19 vaccines were classified according to basic emotion categories using sentiment analysis. The resulting dataset was used for training and testing machine learning classifiers. Results: It was determined that 7.50% of the tweeters had positive, 0.59% negative, and 91.91% neutral opinions about the COVID-19 vaccination. When the accuracy values of the ML algorithms used in this study were examined, it was seen that the XGB algorithm had higher scores. Conclusions: Three out of four tweets consist of negative and neutral emotions. The responsibility of professional chambers and the public is essential in transforming these neutral and negative feelings into positive ones. © 2022 Cambridge University Press. All rights reserved

Using Artificial Intelligence in the COVID-19 Pandemic: A Systematic Review

Artificial intelligence applications are known to facilitate the diagnosis and treatment of COVID-19 infection. This research was conducted to investigate and systematically review the studies published on the use of artificial intelligence in the COVID-19 pandemic. The study was conducted between April 25 and May 6, 2020 by scanning national and international studies accessed in "Web of Science, Google Scholar, Pubmed, and Scopus" databases with the keywords ("Coronavirus" or "COVID-19") and ("artificial intelligence" or "deep learning" or "machine learning"). As a result of the scanning process, 1495 (Google Scholar: 1400, Pubmed: 58, Scopus: 30, WOS: 7) studies were accessed. The studies were first examined according to their titles, and 1385 studies, which were not related to the research topic, were not included in the scope of the research. 50 articles, which did not meet the inclusion criteria, were excluded. The abstract and complete texts of the remaining 60 studies were scanned for the study's inclusion and exclusion criteria. A total of 10 studies, consisting of reviews, letters to the editor, meta-analysis studies, animal studies, conference presentations, studies not related to COVID-19, and incomplete studying protocols, were excluded. There were 50 studies left. 9 articles with duplication were identified and excluded. The remaining 41 studies were examined in detail. A total of 26 studies were found to meet the criteria for the systematic review study. In this systematic review, AI applications were found to be effective in COVID-19 diagnosis, classification, epidemiological estimates, mode of transmission, distribution, the density of lesions, case increase estimation, mortality/mortality risk, and early scans. © 2022 Tehran University of Medical Sciences. All rights reserved

The effects of education on foot care behaviors and self-efficacy in type 2 diabetes patients

Background: Diabetic foot significantly affects the quality of life of patients with diabetes. It leads to loss of labor force, psychosocial trauma, and high treatment costs due to serious morbidity and mortality. Nurses have an important responsibility to improve the metabolic status of individuals with diabetes, to protect them from foot complications, and to teach patients foot care skills. Aim: This study investigated the effects of education on type 2 diabetes patients regarding diabetic foot care and self-efficacy. Materials and Methods: This quasi-experimental study was conducted from February to July 2016 in hospitals located in the city of Balıkesir in Turkey with type 2 diabetes patients who were admitted to the internal medicine clinic and monitored by the endocrinology and internal medicine outpatient clinics. G*power 3.1.9.2 software was used to calculate the sample size of 94 people with a 5% type 1 error, and 90% power. The study was carried out with stratified randomization, and a questionnaire was administered to the experimental and control groups. The experimental group received training, and both groups' scores on the Diabetic Foot Behavior Questionnaire [Appendix 1] and the Diabetic Foot Care Self-Efficacy Scale [Appendix 2] were compared after three months. The t-test, the paired t-test, and the Chi-square test were used. Results: While the self-efficacy and the foot care behavior scores of the control group did not show any differences (P > 0.05), the experimental group's scores were significantly higher (P 0.05). The control group's self-efficacy and foot care behavior scores on the pre-test and final test were similar, while the experimental group's scores increased (P 0.05). Conclusions: Starting from the diagnosis of diabetes, it is advisable to carry out foot assessments and to follow up with diabetics who received foot care education to increase their self-efficacy, to make foot care a habit, and to re-evaluate missing or incorrect practices during check-ups