Aim COVID‐19 pandemic has created a serious psychological impact worldwide since it has been declared. This study aims to investigate the level of psychological impacts of COVID‐19 pandemic on Turkish population and to determine related factors. Methods The study was carried out by using an online questionnaire using the virtual snowball sampling method. The sociodemographic data were collected on the following subjects: participants’ experience on any signs of infection within the last month, the history of COVID‐19 contact‐treatment‐quarantine, level of compliance with precautionary measures, the sources of information and level of knowledge about the pandemic process and their belief levels on the knowledge they acquire. Besides, the questions that take place in the Depression, Anxiety and Stress Scale‐21 (DASS‐21), and “Impact of Events Scale‐Revised (IES‐R) were asked to participants. Results Of the 3549 participants, anxiety was found in 15.8%, depression in 22.6%, stress in 12.9%, and psychological trauma in 20.29% based on moderate and above levels. Female gender, young age, higher education level, being single, high monthly income, presence of psychiatric illness, a large number of people living together, having any signs of infection, and contact history with COVID‐19 infected person or contaminated object are identified as risk factors that may increase psychological impact. Compliance with the rules was found to reduce the risk of psychological response. Conclusions The risk factors for the psychological impact of COVID‐19 pandemic, and acknowledging these factors can help to formulate the interventions to reduce the stress levels of the population.

Objective: Hypothesis testing, correlation, and regression analysis are statistical methods developed to be used in the statistical inference process which is the main purpose of all scientific studies. The purpose of this study is to develop a web-based application using the Shiny package in R software, which allows the evaluation of the results of scientific research to be made in a simpler, easier and understandable way. Methods: In this study, the tests and techniques developed in the software were applied to the data derived from the simulation. This web tool will be updated upon the updated R software packages, including ggplot2, shiny, reshape, plotly, shinydashboard, dplyr, plyr, tinytex, DT, rhandsontable, shinyjs, tools, readxl, foreign, shinyWidgets, shinyLP, shinyjqui, stringr, olsrr, perturb, mctest, relaimpo, MASS, MKmisc, aod, caret, shinydashboardPlus, rmarkdown. Scripts were written for calculations that could not be done by these packages. Results: In the web-based software developed to perform statistical analyses, the results and outputs of the derived dataset were interpreted. Conclusion: The developed interactive user-friendly web application is freely accessible through http://biostatapps.inonu.edu.tr/IAY. In future studies, it is aimed to strengthen the software by adding modules that perform different multivariate statistical analyzes.

Aim: Differentiated thyroid cancer (DTC) is a common type of cancer that originates in the thyroid gland. This study aimed to predict the recurrence of differentiated thyroid carcinoma, in patient with well-DTC, using explainable machine learning (XAI) models. Material and Method: The study utilized a dataset from the UCI Machine Learning Repository, which included 383 patients and 13 candidate predictors. After a variable selection process using distance correlation, only four predictors (Response, Risk, T, and N) were retained for model building. Two XAI models, Fast Interpretable Greedy-Tree Sums (FIGS) and Explainable Boosting Machines (EBM), were employed. Results: The EBM model slightly outperformed the FIGS model in terms of accuracy. The study found that the most influential predictors of Well-DTC recurrence were the response to DTC treatment, risk status according to the American Thyroid Association classification, tumor size (T), and lymph node metastasis (N). Conclusion: In conclusion, this study successfully identified key risk factors for DTC recurrence using XAI models, providing interpretable insights for clinical decision-making and potential for personalized treatment strategies.