ABSTRACT
Evaluating prospective graduates' health literacy profiles before they enter the job market is crucial. Our research aimed to explore the health literacy levels of medical and health students by assessing their ability to obtain health-related information, understand healthcare systems, use e-health, and be informed about vaccination as well as to explore the factors associated with health literacy. Short versions of the HLS19-Q12 were used for a cross-sectional survey that was carried out among 1042 students enrolled in various medical and health educational programs at three medical universities in Kazakhstan between September and November of 2023. Additionally, instruments such as Digital Health Literacy (HLS19-DIGI), Navigational Health Literacy (HLS19-NAV), and Vaccination Health Literacy (HLS19-VAC) were employed. The score of General Health Literacy was 88.26 ± 17.5. One in eight students encountered difficulties in Vaccination Health Literacy. Despite overall high health literacy, Navigational Health Literacy posed challenges for all students. The Public Health students exhibited the highest General Health Literacy (91.53 ± 13.22), followed by students in Nursing, General Medicine, other educational programs (Dentistry and Biomedicine) and Pharmacy. Financial constraints for medication and medical examinations significantly influenced health literacy across all types of individuals. Since comprehensive health literacy instruction or interventions are still uncommon in the curricula, it seems reasonable to develop and incorporate appropriate courses for medical and health educational programs.
ABSTRACT
BACKGROUND: Our study aimed to assess how effective the preventative measures taken by the state authorities during the pandemic were in terms of public health protection and the rational use of material and human resources. MATERIALS AND METHODS: We utilized a stochastic agent-based model for COVID-19's spread combined with the WHO-recommended COVID-ESFT version 2.0 tool for material and labor cost estimation. RESULTS: Our long-term forecasts (up to 50 days) showed satisfactory results with a steady trend in the total cases. However, the short-term forecasts (up to 10 days) were more accurate during periods of relative stability interrupted by sudden outbreaks. The simulations indicated that the infection's spread was highest within families, with most COVID-19 cases occurring in the 26-59 age group. Government interventions resulted in 3.2 times fewer cases in Karaganda than predicted under a "no intervention" scenario, yielding an estimated economic benefit of 40%. CONCLUSION: The combined tool we propose can accurately forecast the progression of the infection, enabling health organizations to allocate specialists and material resources in a timely manner.
ABSTRACT
BACKGROUND: Since the start of the COVID-19 pandemic, scientists have begun to actively use models to determine the epidemiological characteristics of the pathogen. The transmission rate, recovery rate and loss of immunity to the COVID-19 virus change over time and depend on many factors, such as the seasonality of pneumonia, mobility, testing frequency, the use of masks, the weather, social behavior, stress, public health measures, etc. Therefore, the aim of our study was to predict COVID-19 using a stochastic model based on the system dynamics approach. METHOD: We developed a modified SIR model in AnyLogic software. The key stochastic component of the model is the transmission rate, which we consider as an implementation of Gaussian random walks with unknown variance, which was learned from real data. RESULTS: The real data of total cases turned out to be outside the predicted minimum-maximum interval. The minimum predicted values of total cases were closest to the real data. Thus, the stochastic model we propose gives satisfactory results for predicting COVID-19 from 25 to 100 days. The information we currently have about this infection does not allow us to make predictions with high accuracy in the medium and long term. CONCLUSIONS: In our opinion, the problem of the long-term forecasting of COVID-19 is associated with the absence of any educated guess regarding the dynamics of ß(t) in the future. The proposed model requires improvement with the elimination of limitations and the inclusion of more stochastic parameters.
