SU+ Digital Repository

SU+ is an online repository for the preservation and promotion of assorted digital content at Strathmore University

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Communities in DSpace

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Conferences / Workshops / Seminars + 474
Documents and Proceedings of Conferences, Seminars, Workshops (and more) held at Strathmore University
Digital Archives 178
Assorted collections of resources covering various subject themes contributed by Faculty and Library Staff
Reports / Policies + 10
Public reports and policy documents
Research / Researchers / Publications 4401
Researcher Profiles / Conference presentations / Published research articles / Faculty and Corporate research outputs
Strathmore Heritage Collection 41
A digital chronicle of the History of the University presented through a mix of pictures, videos and digitized publications

Recent Submissions

Assessing the long-term financial risk protection impact of COVID-19 an analysis of private health insurance practices in Kenya
(Strathmore University, 2024) Mwanzia, M.
The Covid-19 pandemic has profoundly impacted healthcare utilization and spending trends, posing unprecedented challenges for health insurance providers worldwide. In Kenya, private health insurers faced significant financial strains as they navigated the complexities of the pandemic. This study assessed the long-term financial risk protection impact of the Covid-19 pandemic on private health insurance practices in Kenya, with a focus on identifying key challenges, lessons learned and actionable recommendations for enhancing insurance coverage in future pandemic scenarios. Through an exploratory research design, qualitative data were collected from top managers across 12 private insurance companies in Kenya, including financial, marketing and underwriting managers. Using the framework approach analysis method, patterns and relationships in the data were identified. Findings revealed insights into the effectiveness of pandemic response strategies adopted by private insurers, highlighting successful initiatives such as the rapid adoption of telemedicine services and digital platforms to enhance accessibility and continuity of healthcare services during lockdowns and movement restrictions, and areas for improvement that include, enhancing affordability for clients, addressing operational delays stemming from remote work arrangements, and strengthening IT infrastructure to support seamless service delivery.. Recommendations include enhancing communication with policyholders, diversifying coverage options, and investing in digital infrastructure to support remote work and service delivery. These insights provide valuable guidance for policymakers and stakeholders in the private health insurance sector, facilitating informed decision-making and enhancing resilience in future health crises.
The Effect of use of inventory management systems on availability of essential medicines and service delivery - a case study of Ishiara Level 4 Hospital
(Strathmore University, 2024) Waneno, N. S.
The study examined the effect of use of a digitized inventory management on availability of essential medicines and hence service delivery in a public hospital, Ishiara Level 4 Hospital in Embu County. The objectives of the study were to assess the current inventory management system, to evaluate the effect of staff skills and competence on inventory management and to forecast inventory management to aid in service delivery. This was done by use of primary data from questionnaires distributed to a sample size of 79 out of the 130 health workers directly involved in service provision at the facility. Secondary data was derived from the DHIS tool on trends of patient visits over a period of ten years to the dental clinic in the facility. The variables in the secondary data were the number of patients attended to and the number of procedures done. Analysis of the primary data was done using descriptive analysis and regression equation and analysis. Secondary data was analyzed using descriptive analysis using the excel tool, trend analysis, correlational analysis and predictive analysis by use of the ARIMAX and SARIMAX models. The main findings of the study were that inventory management system in the facility has not been able to ensure availability of essential medicines. Staff competence and skill affect inventory management use and hence service delivery. Therefore, there needs to be more facilitation of staff in terms of training and more emphasis needs to be put on training and facilitation of staff. From the secondary data, it was determined that predictive analysis can be useful in ensuring availability of essential medicines through improvement of inventory management. This would reduce cases of stock outs and patient referrals from the facility with an improvement in service delivery. Key terms; Inventory Management, Service delivery, Demand forecasting.
Adherence to infection prevention and control practices among critical care staff at the Kenyatta National Hospital in Kenya
(Strathmore University, 2024) Mutia, G. W.
Compliance with infection prevention and control (IPC) practices is important for the provision of safe, quality, and efficient healthcare services. This study assessed healthcare worker (HCW) compliance with IPC practices and associated factors at the Kenyatta National Hospital intensive care units. The hospital-based cross-sectional study involved HCWs who were directly involved with patient care across the Hospital’s ICUs. These included doctors (consultants, registrars, and medical officers), nurses, clinical officers, and allied professionals. A total of 189 providers were recruited using consecutive sampling, with care taken to include all key cadres. Data were collected using a structured self-administered questionnaire uploaded on Google Forms. Information was collected on compliance levels and the organizational and contextual influences, and analysis was done using SPSS version 28. Of the total respondents, 62% were aged between 31- 40 years, 63% were female, 62% were bedside nurses, and 98% had received some form of training on IPC, out of whom 91% said the training was conducted as part of in-service training by the Hospital. The findings established that 47.1% had optimal overall compliance with standard IPC practices. As for compliance with individual IPC components, 54.5% were optimally compliant with the use of PPEs, 40.2% were optimally compliant with the safe disposal of sharps, and 88.9% were optimally compliant with the appropriate disposal of waste. Adequate management support and work safety climate, absence of job hindrances, and education and training of HCWs on standard IPC practices, were the organizational and environmental factors that were found to have a significant correlation with adherence to standard IPC practices. Adequate knowledge and a good attitude toward IPC practices were the individual factors that were associated with adherence to standard IPC practices. In conclusion, a multifaceted approach encompassing both organizational-level and individual-level strategies should be employed in improving IPC compliance among HCWs.
Machine learning for multi-class identification of Gender Based Violence on social media
(Strathmore University, 2025) Mutahi, E. W.
This study aims at showcasing the use of Machine Learning algorithms in the classification of forms of Gender Based Violence using Social Media data. Data mining processes were used to fetch 1 million tweets from January 2012- January 2023 from Twitter using keywords that identified Gender Based Violence. 160,000 tweets were manually labeled to identify the form of Gender Based Violence namely; physical violence, economic violence, sexual violence and emotional violence. The rest of the data was saved in SQLite as a GBV database. The tweets were filtered and analysed using Natural language Processing techniques such as Exploratory Data Analysis, Sentiment analysis and Topic Modelling. Machine learning algorithms such as Naïve bayes, Random Forest and Support Vector Machines were trained using the labelled data in order to predict the form of Gender based violence on the tweets. The models were evaluated using Accuracy, Precision, Recall, F1 score and AUC as the performance metrics. SVM using Glove features had the highest F1 score of 61% and an accuracy score (62%) followed by the Multinomial Logistic Regression at an F1 score of 60% and an accuracy of (61%). A web application was designed on streamlit to host the results of the study and allow users to interact and get the predicted form of GBV from text inputs or from data selected from the GBV database. Logistic Regression and SVM were found to show superiority in the detection of cyberbullying on twitter without the involvement of victims (Muneer,2020). In this study, the classification of GBV was intended to inform key stakeholders on the extent and form of GBV incidences and to aid in the identification or structuring of programs that can offer timely and relevant support to survivors of Gender Based Violence. The insights can be used to build social media-based interventions to support survivors immediately they are identified. Key words: Gender Based Violence (GBV), social media, Machine Learning, Classification
Development of drowsiness detection system using machine learning and image processing techniques
(Strathmore University, 2024) Omondi, D.
Since drowsy driving is a major problem in Kenya, which has led to multiple fatal accidents, it has raised discussions that seek for a set of solutions. Several fatalities and injuries resulting from road accidents caused by drowsy driving have been recorded and it has been identified to be a significant problem. Studies that have been conducted in this field have identified interventions in terms of detection and alert systems that can offer solutions to this problem. This study mixes these categories to come up with a machine learning algorithm that records the driver, analyses their condition, processes the information and gives feedback to get the driver back to normal. The data used in training the machine learning model used for this study was extracted from secondary sources, including the internet. The data was then cleaned before use. However, the accuracy of the model determines its application. The model developed for this study generated a weighted average precision of 86% with a recall of 83%, an 84.5% accuracy and an F1-score of 83%. These results are relatively high compared to many models that have been used by previous researchers. This shows how applicable the model is in real situations and how much the other models need to be improved. However, limitations like small size of training data and low processing power of the equipment used should be addressed in future studies for better outcomes.