Search

BACKGROUND: Online learning has the potential to increase accessibility to high quality and cost-effective resources in prevention of risk behaviors. The aim of this pilot study was to assess the experience of university students with the comprehensive online course on prevention. METHODS: In this pilot study, an online questionnaire was administered to 51 Czech and 31 Ukrainian university students who completed the online Introduction to Evidence-based Prevention (INEP) full semester course between February 2022 and February 2023. Students were asked about their experience with INEP represented by 17 distinct features. Data were analyzed by descriptive statistics and mean comparisons tests. RESULTS: Students reported high overall satisfaction with INEP and with its respected features. The Structure and the Relevance features of INEP have been especially appreciated, while the Quizzes feature was perceived as only average by most students. INEP seemed to encourage most students (82%) to take other e-learning courses. CONCLUSION: The online INEP course received favorable feedback from university students across two distinct settings. INEP holds potential for broader integration into university study programs. These findings add to the ongoing discourse regarding enhancements in the education of future prevention professionals, making them relevant to practitioners, policymakers, and university-level decision-makers. [ABSTRACT FROM AUTHOR]

BACKGROUND: In many countries families have rarely been involved in addressing treatment and interventions for family members with substance use disorders (SUDs). The aims of this study were to measure the influence of substance use on users’ family members and to evaluate what and if any interventions were conducted by and with family members in the three countries of Kazakhstan, Pakistan, and Ukraine. METHODS: A convenience sample approach was used to identify the involvement of families in the treatment and recovery process of a subject using substances and to provide countryspecific data from Pakistan, Kazakhstan, and Ukraine. RESULTS: The survey yielded 2174 respondents from the three countries, including 1099 responses from relatives of a subject who used or uses substances and 1075 from subjects who use or used psychoactive substances. In total, 47% of the relatives indicated that they did not receive treatment interventions and 87% believed that treatment could be an effective option to address the use of substances. CONCLUSIONS: A significant majority of the relatives in Pakistan, Kazakhstan, and Ukraine did not receive any therapeutic or other support while living with a subject with an SUD. Unsurprisingly, family members often regarded the best way to treat SUDs as being to isolate the subject. However, in all three countries most of the subjects with experience of substance use identified the family as the main factor that could prevent them from using substances. [ABSTRACT FROM AUTHOR]

BACKGROUND: The high prevalence of HIV and substance use disorders (SUD) in Ukraine remains a significant public health problem. A well-trained, recovery-oriented national workforce to enhance SUD treatment associated with HIV care is needed to reduce substance use and HIV-related burden in the country. METHODS: The Ukraine Addiction Transfer Technology Center (Ukraine ATTC) was established in 2017 to provide training and technical assistance on the intersection of HIV and SUD to support efforts to control the HIV epidemic in Ukraine. Data were derived from Ukraine ATTC initiatives in its first three years of operation. RESULTS: The Ukraine ATTC's activities aligned with the Efficiency, Impact, Sustainability, Partnership and Human Rights action agendas of the U.S. President's Emergency Plan for AIDS Relief (PEPFAR). A total of 985 participants took part in one or more Ukraine ATTC activities over the three years. Activities included 443 hours of face-to-face training, 28 hours of online training, and 292 hours of posttraining supervision and implementation support. Among its most significant activities, the Ukraine ATTC trained a cadre of 35 certified National Trainers (NTs) to scale up training and educational activities on HIV, SUD, and mental health throughout the country. Between 2019-2020, NTs conducted 24 Project ECHO-style trainings with 440 persons. CONCLUSIONS: In addition to training a large number of professionals in evidencebased practices, the Ukraine ATTC facilitated the training of others throughout Ukraine, thus expanding the reach of the evidence-based curriculum beyond its core activities. A trained workforce and curricula promote the sustainability of these efforts and serve as an important national resource. [ABSTRACT FROM AUTHOR]

BACKGROUND: In Ukraine, substance use disorders (SUDs) and rates of HIV infection are growing, the number of specialists does not meet country's addiction treatment demands, and the quality of the care is often insufficient and does not cover the current treatment needs. A necessary first step to improving care for people with SUD in Ukraine is training needs assessment of addiction specialists. METHODS: A survey instrument, "Training Needs Survey for SUD Specialists," developed by the Substance Abuse Mental Health Services Administration (SAMHSA), an agency of the United States Department of Health and Human Services, was translated and adapted by the Ukrainian team of researchers. All institutions and organizations that identified as providing treatment for SUDs received the survey. This article details the training needs of people working with patients with SUDs based on a representative sample of addiction specialists at different types of treatment organizations in all regions of Ukraine (except the occupied territories). RESULTS: Different specialty groups had different training needs based on the individual's role in the treatment process. Addiction counselors expressed the greatest overall need for training. Compared to other specialists, the managers for social, educational, and psychological work with incarcerated individuals expressed a lower need for training. Training in medication-assisted therapy was not a high priority for respondents from any specialty group. CONCLUSIONS: The survey assessing the training needs of people working with patients with SUDs identified a wide range of interests that varied by the respondent's role and profession. Existing training courses and programs often do not cover complex issues of interest to specialists or reflect a multidisciplinary approach. Therefore, current training courses require changes that align with modern concepts of addiction treatment. [ABSTRACT FROM AUTHOR]

Healthcare workers experience overwhelming occupational and psycho-emotional stress during COVID-19 pandemic. Occupational stress leads to emotional burnout, can cause anxiety, depression and other psychopathological symptoms, influencing the quality of medical care. Existing situation necessitates to define risk factors, influencing occupational stress in healthcare workers in order to develop ways and methods to overcome it. Aim of the study was to identify socio-psychological factors associated with occupational stress in healthcare workers during the COVID-19 epidemic in Ukraine. Using a specially designed questionnaire, an online survey of 1,100 medical workers was conducted in all regions of Ukraine. Questioning was carried out over three weeks from 30.03.2020 to 21.04.2020, during the period of quarantine. The majority of respondents were female medical workers (79.9%). Age of respondents: up to 30 years – 179 (16.2%), from 31 to 60 – 824 (75.0%), over 60 – 97 (8.8%), among them doctors – 695 (63.1%) 236 nurses (21.5%), more than a third are general practitioners. Direct care for patients with COVID-19 was provided by 170 (15.5%) medical personnel. The analysis of the results of the questionnaire enabled to determine the peculiarities of medical worker`s response to the situation in connection with COVID-19 and highlight the emotional and behavioral factors associated with stress that are significant for most respondents. The study answered the questions regarding the perceived risk associated with the spread, contamination and death related to coronavirus. The most significant factors affecting the occurrence of stress in medical workers are identified. Along with this, factors contributing to stress coping have been identified as well. For subsequent statistical processing, all respondents were divided into two groups depending on the severity of the anxiety/fear. Comparison of these groups by socio-demographic indicators was done using the test χ². There were no statistically significant differences between groups in all socio-demographic indicators, except for age. That is, only the age factor affected the severity of anxiety/fear among healthcare workers due to COVID-19 (p<0.05). Comparative analysis to assess the severity of stress in groups was performed using the Mann-Whitney U test. Healthcare workers with severe anxiety/fear have a high risk for development of occupational stress during a COVID-19 pandemic (p<0.01). In conclusion, the need for the development and implementation of methods for the prevention of occupational stress in medical workers in the current epidemic situation is substantiated. [ABSTRACT FROM AUTHOR]

Groundwater quality is vital for public health, agriculture, and industry, especially in regions like Telangana, India. This study analyses and predicts post-monsoon 2020 groundwater quality using data from the Telangana State Groundwater Department. We employed Linear Regression and Random Forest Regression to predict key parameters: pH and Total Dissolved Solids (TDS). Exploratory data analysis revealed significant correlations, such as between TDS and Electrical Conductivity (E.C). The Linear Regression model for TDS performed exceptionally well, with an R2 of 0.985, while the Random Forest model also showed strong results. However, both models exhibited moderate accuracy in predicting pH. The study demonstrates the effectiveness of machine learning models in predicting groundwater quality, offering valuable tools for groundwater management. These findings can aid policymakers and environmental managers in making informed decisions to safeguard water resources.

Ensuring access to safe drinking water is a fundamental public health priority. Traditional methods for assessing water quality are laborintensive and require specialized equipment, which may not be feasible for continuous monitoring. This study explores the use of machine learning models to predict water potability based on various chemical properties. Specifically, we evaluate the performance of Logistic Regression and Random Forest models in the presence of class imbalance, a common issue in environmental datasets. To mitigate this, we apply the Synthetic Minority Over-sampling Technique (SMOTE). Our results indicate that before applying SMOTE, both models exhibited a strong bias towards the majority class (non-potable water), achieving an accuracy of 69.36% and a ROCAUC of 0.63. However, the application of SMOTE significantly improved the model’s ability to identify potable water samples, particularly for the Random Forest model, which achieved an accuracy of 67.07% and a ROCAUC of 0.64 post-SMOTE. In contrast, the Logistic Regression model showed a decline in performance after SMOTE, suggesting the need for further optimization or alternative approaches. This study highlights the importance of addressing class imbalance in machine learning tasks, especially for critical applications like water quality assessment. Our findings suggest that the Random Forest model, combined with SMOTE, offers a robust solution for predicting water potability. These insights can aid environmental scientists and public health officials in implementing more efficient and accurate water quality monitoring systems. Future research should explore a broader range of models and advanced techniques to further enhance prediction accuracy.