http://hdl.handle.net/123456789/19 Thu, 09 Apr 2026 23:31:05 GMT 2026-04-09T23:31:05Z http://hdl.handle.net/123456789/1240 Work-Life Integration Strategies and Performance of Health Practitioners in Public Health Facilities in Kirinyaga County, Kenya Wairia, G In the present-day quickly shifting work environment, work-life integration has become a vital aspect in defining individual performance and overall organizational success. Unlike conventional work-life balancing techniques, work-life integration stresses the seamless mixing of professional and personal duties, offering individuals more flexibility and control over their time. The general objective of the study was to investigate the effect of work-life integration strategies on the performance of health practitioners in public health facilities in Kirinyaga County, Kenya. The specific objectives of the study were to: establish the effect of technology, flexible working arrangements, welfare programs, and leave programs on the performance of health practitioners in public health facilities in Kirinyaga County, Kenya. The study was anchored on the following theories: Social Exchange Theory, Spillover Theory, Role Theory, and Effort-Reward Imbalance (ERI) Model. A descriptive research design was chosen, with a sample size of 279 drawn from a target population of 920 using stratified random sampling technique using Yamane's (1967) formula. Data was collected using 5-point Likert Scale questionnaire. Tables and percentages were applied for descriptive statistics, while Multiple regression analysis using SPSS (version 26) was used to determine the effect of the work-life integration strategies on performance. In addition, ANOVA and F-tests were conducted to test the overall significance of each regression model. The results revealed that technology had a statistically significant effect on employee performance, with an Adjusted R Square of 0.712, indicating that technology accounted for 71.2% of the variance in employee performance. Flexible working arrangements also showed a significant influence, with an Adjusted R Square of 0.638, meaning they explained about 63.8% of the variation in employee performance. Welfare programs demonstrated a notable effect on employee performance, indicated by an Adjusted R Square of 0.595, suggesting that welfare programs contributed to 59.5% of the changes in employee performance. Finally, leave programs showed a significant influence with an Adjusted R Square of 0.644, implying that leave programs accounted for about 64.4% of the variance in employee performance. Regression analysis found that these work-life integration strategies collectively explained 84.5% of the variation in performance, with technology accounting for the biggest part. The results underline the necessity of implementing these work-life integration strategies into organizational practices to maximize health practitioners' performance, work satisfaction, and general well-being. This research presents empirical data supporting the adoption of work-life integration strategies in public health settings and suggests their application to increase staff productivity, retention, and healthcare delivery in Kirinyaga County. Mon, 05 May 2025 00:00:00 GMT http://hdl.handle.net/123456789/1240 2025-05-05T00:00:00Z http://hdl.handle.net/123456789/1239 Modeling Social Factor and Faulty Health System On The Dynamic of Childhood Diarrhoea in Low Income Population Muiri, C Mathematical modeling of infectious diseases offers insights into the core processes of disease propagation and transmission and assesses the potential severity of an epidemic. The main way that diarrhoea, an illness symptom caused by parasite, viral, or bacterial pathogens, spreads is through fecal matter-contaminated water. Stress, whether experi- enced in childhood or adulthood, can significantly influence the development of bowel disease. Surveillance studies conducted in wellness facilities may underestimate the true burden of disease, especially in settings with limited resources where many individuals do not seek medical care. In addition to limiting access to healthcare, low socioeconomic level can have an impact on housing conditions, food, and other elements that raise the risk of contracting infectious diseases. Proper management of childhood stress and im- provements in healthcare systems significantly reduce diarrhoea incidence and associated complications. Numerous models have been suggested in scholarly works to control child- hood diarrhoea disease, however there is scanty information about modeling social factor and faulty health system regarding the trends and factors influencing diarrhea in children in Kenya. This study developed a mathematical model of social factor and faulty health system on the dynamic of childhood diarrhoea in Kenya. The model was based on a system of nonlinear first-order ordinary differential equations. It demonstrated using the Jacobian matrix that the local and global stability analysis of the disease dies out and reaches a disease-free equilibrium when the basic reproduction number (R0) is less than 1. Specifi- cally, R0 was calculated to be 0.008278, indicating that with appropriate care for children under five during a diarrhoea outbreak, the disease can be effectively controlled. Con- versely, if R0 is greater than 1, the disease may persist, leading to the establishment of an endemic equilibrium. Using center manifold theorem to calculate bifurcation analysis demonstrated a forward bifurcation showing the disease will die out at sometime. Simula- tion studies using the model parameters was calculated to show how social factor (stress) and faulty health system propagate childhood diarrhoea as seen in figure 2 and 5. Under five children with stress and also subjected to faulty health system usually suffer severely as compared to those without stress and in normal health facilities. The findings of this study will offer important insights to relevant stakeholders, informed laboratory technicians and field experts by demonstrating the effect of stress and faulty health system that will aid in development of new intervention strategies which will help to reduce the spread of under five childhood diarrhea during an outbreak that will otherwise remain unknown leading to better designs for future development. Fri, 05 Sep 2025 00:00:00 GMT http://hdl.handle.net/123456789/1239 2025-09-05T00:00:00Z http://hdl.handle.net/123456789/1238 Determinants of Preeclampsia and Maternal and Perinatal Outcomes Among Women in Narok County, Kenya Kibet, R Preeclampsia is a critical hypertensive disorder in pregnancy that significantly impacts maternal and neonatal health outcomes. The World Health Organization (WHO) recognizes it as a key direct factor in global maternal mortality, especially in low- and middle-income countries. The disease is prevalent in Sub-Saharan Africa, posing a considerable threat to women and their infants. In resource-limited settings like Narok County, Kenya, delays in accessing healthcare and gaps in screening and treatment have worsened the impact of preeclampsia. This study aimed to identify the key factors associated with preeclampsia and to evaluate its effects on mothers and their newborns in Narok County. A retrospective cross-sectional review of 5,801 delivery records from January to December 2023 was conducted, from which 331 were systematically sampled, and 217 met the inclusion criteria for final analysis using a standardized data extraction tool. Descriptive analysis revealed that most preeclampsia cases (88.2%) were severe and commonly presented with symptoms such as severe headache, followed by visual disturbances, and epigastric pain. Cesarean section was performed in 17.6% of cases, maternal complications occurred in 58.8%, and the maternal mortality rate was 5.9%. Common maternal complications included HELLP syndrome and eclampsia. Neonatal outcomes were poor, with 27.8% stillbirths, 50% low birth weight, and 44.4% low Apgar scores. Neonatal complications and deaths occurred in 7.7% and 5.6% of cases, respectively. Inferential analysis using Chi-square or Fisher’s exact tests followed by binary logistic regression identified multiple gestation (OR = 3.46; 95% CI: 1.04–11.50; p = 0.043) and primigravidity (p = 0.026) as significant determinants of preeclampsia. Rural residence (OR = 4.50; p = 0.054) showed no significant association. Preeclampsia poses a substantial burden on maternal and perinatal health in Narok County. Multiple gestations and first-time pregnancy were identified as key determinants, and preeclampsia resulted to higher rates of antenatal complications, adverse maternal outcomes, and poor neonatal outcomes such as prematurity and low birth weight. Strengthening antenatal care through early screening, risk-based monitoring, and timely interventions is essential to mitigate these risks. Thu, 04 Sep 2025 00:00:00 GMT http://hdl.handle.net/123456789/1238 2025-09-04T00:00:00Z http://hdl.handle.net/123456789/1237 A Machine Learning-Based Packet Sniffer for Detection And Classification of The Denial of Service Attack Packets at The Network Layer Kipkorir Peacemark The research study was on modelling a packet sniffer utilizing machine learning techniques to identify denial of service (DOS) attack packets at the network layer of the OSI model. Cyber threats and attacks have continued to evolve in complexity and sophistication, posing significant risks to the network infrastructure and sensitive data's availability, confidentiality, and integrity. The overall purpose of the research study was to capture and interpret packets transmitted over a local area network to detect and capture the DOS threats within the Open Systems Interconnection Model (OSI) network layer. This layer is prone to several attacks for instance, denial-of-service, routing protocol attacks, Port scanning and enumeration, and fragmentation-based attacks. However, this study delved into detecting and capturing the denial of service threats at the network layer. Some examples of DOS attacks are UDP flood which sends a significant quantity UDP (User Datagram Protocol) packets to the targeted systems and thereby exhausting network resources, ICMP flood which transmits a significant quantity of Internet Control Message Protocol (ICMP) packets to overwhelm network devices, SYN flood which takes advantage of the TCP three-way hand-shake procedure by sending a lot of SYN requests without carrying out the necessary handshake, using server resources and blocking valid connections. Essential components extracted from Ethernet frames comprise TCP segments, ICMP packets, IPv4 packets, and associated flags. IPv4, a crucial protocol in Internet communication, enables routing and logical addressing, forming the Internet's backbone. The Internet Control Message Protocol (ICMP) facilitates error reporting and the interchange of operational information inside the Internet Protocol suite. Even though internet-based data transmission protocols have expanded, traditional network security measures are frequently insufficient to combat the dynamic environment of cyber threats that target networks used for data transfer. The LightGBM model was successfully trained and implemented for the task of detecting DoS attacks. The study used the CICIDS2018 dataset, which provided labeled network traffic data containing both normal and attack (DoS) instances. The model was trained to classify traffic as either normal or a DoS attack based on various network features. The model's performance was evaluated using several metrics to demonstrate its ability to accurately detect threats at the network layer in a local area network including sensitivity, specificity, and accuracy. The AUC (Area Under the Curve) was particularly high, which indicated that the model was able to effectively differentiate between normal traffic and DoS attacks. Additionally, the F1- score, precision, and recall were balanced, suggesting that the model was capable of identifying attacks while minimizing false positives and false negatives. The model was successful in meeting its primary objective of detecting DoS attacks from network traffic. The performance metrics indicate that LightGBM is a strong candidate for the task, achieving a high AUC and a well- balanced F1-score. This suggested the model achieved good generalization capabilities, and it can effectively distinguish between normal traffic and DoS attack traffic. The main contribution of this work was the development of a LightGBM-based machine learning model for detecting DoS attacks using the CICIDS2018 dataset. Thu, 21 Aug 2025 00:00:00 GMT http://hdl.handle.net/123456789/1237 2025-08-21T00:00:00Z