Journal of Science and Inclusive Development

A short-term treatment outcome and its associated factors of transvesical prostatectomy among patients admitted with Benign Prostatic Hyperplasia at Wolaita Sodo University Comprehensive Specialized Hospital, Southern Ethiopia

2024-09-11T21:23:24+00:00

Benign prostatic hyperplasia is the commonest cause of urinary problems in elderly males, affecting their quality of life. Modalities of treatment include watchful waiting, medical treatment, surgical treatment like transurethral resection, and open prostatectomy. But the outcome and associated factors of prostatectomy among patients with benign prostatic hyperplasia at Wolaita Sodo University Comprehensive Specialized Hospital were not studied. To assess the short-term treatment outcomes and associated factors of transvesical prostatectomy (TVP) among patients admitted with benign prostatic hyperplasia (BPH) in Wolaita Sodo University Comprehensive Specialized Hospital. A facility-based cross-sectional study was conducted from October 1, 2022, to October 30, 2022, among 316 patients diagnosed with benign prostatic hyperplasia. The data were collected by reviewing medical records, entered into
EpiData version 3.1, and analyzed using SPSS version 25. Descriptive statistics were used to describe the data. A bi-variable and multivariable logistic regression analysis was used to identify factors associated with the outcome of transvesical prostatectomy. Adjusted odd ratio (AOR) (with a 95% confidence interval) was used to report association, and significance was declared at a P-value <0.05. Ninety-six percent (96.2%) of patients presented with severe symptoms, and 83.9% had pre-operation catheterization. The overall complication rate was 42.4%, and surgical site infection was 38.3%. Duration of symptoms [AOR = 9.05, 95% CI (2.16-37.88)] and type of skin incision [AOR = 18.98, 95% CI: 1.67–208.45] were significantly associated with complications of TVP. The complications seen in this study are higher than in other studies in Ethiopia and some other African countries. Duration of symptoms and type of skin incision were factors significantly associated with outcome. Improving community awareness about the nature of the disease and the importance of follow-up both before and after surgery is needed to minimize complications.

Predicting Ethiopian gross domestic product using machine learning model

2024-09-11T21:37:36+00:00

The Gross Domestic Product (GDP) is an extensive indicator that reflects all of a country's economic activity over a certain time period. It calculates the total monetary value of all commodities and services produced within the country's borders. We employed a variety of algorithms and models to forecast Ethiopia's GDP using machine learning, including linear regression, Lasso regression, ridge regression, decision tree regression, random forest regression, gradient boosting regression, support vector machine regression, and neural network regression. Three phases comprise our investigation. First, we collect a dataset consisting of several economic statistics from the National Bank of Ethiopia. The gathered dataset is then preprocessed to ensure machine learning models can use it. Ultimately, we partition the dataset, designating 80% of it for model training and the remaining 20% for performance assessment. We employ a 5-fold cross-validation approach and consider evaluation metrics, including R-squared, mean absolute error, root mean square error, and mean squared error, to assess the efficacy of the model. Among all the models, Ridge Regression performs the best, achieving the lowest root mean squared error of 27,231,241,464.13, the highest R-squared value of 0.9950, a mean squared error of 1.06e+20, and a mean absolute error of 21,552,080,423.90. These results indicate that the model captures 99.5% of the variability in the data. Consequently, using the test dataset, the Ridge Regression model accurately forecasts Ethiopia's GDP.

Farmers' perceptions of climate change and variability and determinants of adaptation strategies and coping mechanism in Ethiopia

2024-09-11T21:49:25+00:00

Climate change and variability are significantly affecting the Ethiopian agricultural sector, which serves as the backbone of the country's economy. The implementation of adaptation strategies and coping mechanisms is influenced by various factors, which are site-specific. Thus, this study aimed to explore farmers' perceptions of climate change and variability and the determinants of adaptation strategies and coping mechanisms in Ethiopia. Data were collected through face-to-face interviews with 133 farming households. The results revealed that farmers' perceptions of local indicators, such as temperature changes and rainfall patterns, underscore the diverse and impactful nature of climate variations on their experiences. The multivariate probit model output showed that the determinants of farmers' responses to climate variability, such as age, family size, educational status, sources of income, types of farming, farming experience, farm size, access to extension services, and availability of seasonal information, were significantly affected (p < 0.01). The impact of education, family size, farming experience, and income sources varies across various facets of agricultural practices, emphasizing the nuanced dynamics of coping mechanisms. Policymakers and agricultural practitioners seeking to develop effective strategies need to consider diverse needs and challenges faced by farmers in adapting to climate variability. Promoting education and improving access to extension services are critical components of successful adaptation strategies and coping mechanisms.

University students’ readiness for E-Learning adoption in Tanzania

2024-09-11T21:58:26+00:00

The importance of e-learning as an education delivery solution in higher education is underscored. However, there is a substantial variation in digital infrastructures and teachers' and learners' perceptions. Hence, before implementing e-learning, it is critical to assess the readiness for e-learning to ensure successful delivery of education in digital environments. Thus, this study assessed students' readiness for e-learning adoption in Tanzanian higher education institutions, uncovering varied readiness levels and influencing factors. Utilizing a cross-sectional design, data was gathered from undergraduate and graduate students at the University of Dodoma between November 2021 and February 2023 through surveys, content analysis, and observation. Findings indicate a moderately high level of e-learning readiness, with varying digital literacy levels. Students exhibited high technological capability and positive e-learning attitudes. Institutional support emerged as crucial, emphasizing the need for e-learning policies, software provision, training programs, and technical assistance to facilitate smooth transitions to digital learning environments. The study informs educational institutions and policymakers on the drivers of students' e-learning readiness, offering actionable insights and strategies for effective e-learning adoption.

Homestead coffee production in Southern Ethiopia: A promising approach to enhance household asset building

2024-09-11T22:08:44+00:00

Ethiopia is one of the largest coffee producers, and the status of coffee production and people's resource endowment are inextricably linked. However, coffee production and its impact on family assets have yet to be fully studied. This study explores the relationship between households' livelihood asset building and their coffee production status in southern Ethiopia, Wolaita zone. Data were collected through face-to-face interviews with 250 households. Employing the data produced from surveys, a composite household livelihood asset index incorporating three components (household ownership, access to agricultural resource endowments, and the empowerment of human capital) was developed. And then, the effect of asset dimensions on coffee production status on household asset building was calculated using step-wise regression. The multivariate analysis showed a significant role of home garden coffee
production on household livelihood asset building in the context of the mixed farming systems of the study area. The two (access to agricultural resource endowments and the empowerment of human capital) out of the three assets latent dimensions were found to be significantly predicted by the farm’s coffee production status. It was recommended that enhancing the asset capital status of rural farm households merits special attention, including their human capital, independent ownership rights over land and other resources, and participation in social processes.