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Recent Submissions
An analysis of the impact of rap music on men’s mental health awareness in Uganda: a case study of Mukono, Uganda
(Uganda Christian University, 2026-06-12) Precious Gift Aloyo
This study analyses the effect of rap lyrics on men's mental health awareness in Uganda. It uses Mukono District as a case study and as a point of reference for the author. Despite an increased number of Ugandan men facing mental health difficulties, societal pressures to define masculinity and mental illness as something "masculine", the stigma attached to living with such conditions, lack of service provision, as well as limited access (or lack thereof in some cases), make for a reluctance toward open discussions with men seeking help or help-seeking behavior. Although rap music is widely popular among young men and often deals with these issues through themes of struggle, coping effectively in the face of suffering, mental illness, such as trauma and identity problems, very few studies have been used to explore its impact on mental illness awareness within the Ugandan context. A qualitative case study design was used in this study targeting young men aged 18–40 years living in Mukono District. Data were gathered through semi-structured interviews, focus group discussions, and content analysis of selected Ugandan and international rap songs. A total of 50 male participants were purposively and snowball-sampled. A thematic analysis was conducted to explore men's perceptions of mental health, their exposure to rap music, and the influence of rap lyrics on mental health awareness, attitudes, and stigma reduction. The findings suggest that rap music serves as a powerful yet complex communication tool. For many listeners, it creates opportunities to engage with conversations about emotional struggles, encourages openness, and helps normalize vulnerability. However, its influence is not uniform. While some rap songs promote self-reflection, emotional expression, and healthy coping mechanisms, others may reinforce harmful stereotypes or unhealthy ways of dealing with psychological challenges. The study highlights the potential of rap music as a culturally relevant and accessible platform for advancing mental health advocacy among men. By connecting with audiences through relatable experiences and language, rap music can contribute to greater awareness and understanding of mental health issues. These findings contribute to both communication and public health scholarship by demonstrating how popular culture can be leveraged to support men's psychological well-being in Uganda. Furthermore, the study offers practical insights for mental health practitioners, musicians, media organizations, and policymakers seeking to develop more targeted, engaging, and effective mental health awareness initiatives.
ANI-LINK: AI-Powered cattle foot-and-mouth disease detection and veterinary care in Uganda
(Uganda Christian University, 2026-06-04) David Buembo; Leon Matabi Kasingye; Teopista Najjuma
Livestock production plays a critical role in rural livelihoods across Uganda, providing income, food security, and economic stability for smallholder farmers. However, the sector continues to face significant challenges due to delayed disease detection, limited access to veterinary services, and fragmented livestock health management systems. These challenges contribute to substantial productivity losses, particularly from diseases such as Foot-and-Mouth Disease (FMD).
This study presents AniLink, an artificial intelligence (AI)-enabled livestock health platform designed to support early disease detection, improve health record management, and enhance access to veterinary services. The system integrates a multi-task deep learning model based on MobileNetV3 for simultaneous cattle verification and FMD detection, alongside digital health records and a mobile- based service interface. A Design Science Research (DSR) approach was adopted to guide system development and evaluation. The model was trained on a combination of publicly available datasets and locally collected images from Gomba District, Uganda. Evaluation was conducted using classification metrics, system performance measures, and user-centred usability testing. Results demonstrate strong model performance, achieving high recall for both cattle detection and FMD classification, along with efficient real-time processing suitable for mobile deployment. Usability evaluation indicates that the platform is accessible to users with varying levels of digital literacy, with positive feedback on ease of use and clarity of outputs. The findings suggest that integrated AI-driven livestock health systems can significantly improve disease detection, decision-making, and access to veterinary support in resource-constrained environments. The study contributes a practical and scalable framework for applying artificial intelligence in agricultural health management.
Glucosense Ecosystem: An Intelligent Clinical Insulin Decision Support and Nutrition Platform for Diabetes Care.
(Uganda Christian University, 2026-06-04) Joy Abaho; Godfrey Mucunguzi; Bachawa Wangolo
Type 1 diabetes is one of the most surging health condition with an approximate number of 369,100 adult cases in Uganda by 2024. There is still a lack of adaptive and personalized tools necessary to manage the disease. The GlucoSense ecosystem focuses on an integrated, full-stack with machine learning to predict insulin intakes, a nutritional system that offers meal recommendations, a meal chatbot, and glucose-guided foods for diabetic patients. The project was built on a three-tier architecture with the frontend layer developed in ReactVite for both the Clinical portal and the Meal plan User Interface, the backend layer follows a FastAPI design and the database layer was built in SQLite and SQLAlchemy.
The results show that the Linear Regression model achieved the lowest error values in predicting insulin dosage with a Mean Absolute Error of 2.98, Mean Squared Error of 11.524 and a Root Mean Squared Error of 3.3947. These results proved that linear Regression was the most stable model and produced fewer dangerous miscalculations compared to random forest, gradient boosting and xgboost. However, all the models showed very low negative R2 values which would probably have occurred because of feature limitations since most features might not have been fully captured.
Ai-powered offline maize streak virus (MSV) detection system for smallholder farmers: design, implementation, and evaluation
(Uganda Christian University, 2026-06-05) Liz Treasure Namatovu; Charles Kidega Omoya; Irwin Mwine
Maize Streak Virus (MSV) remains a highly destructive agricultural pathogen in Sub-Saharan Africa, severely undermining crop yields and threatening smallholder food security. While laboratory diagnostics like PCR and LAMP offer high sensitivity, they remain financially and logistically inaccessible to rural farmers who lack timely extension support. Deep learning and mobile computer vision present a transformative alternative for automated plant disease detection. However, most existing digital solutions assume persistent internet connectivity, cloud infrastructure, or premium hardware, rendering them impractical under the rigid network and power constraints of rural African fields. To bridge this technology adoption gap, this project presents MaizeGuard, an inclusive, dual platform, entirely offline-first AI-powered diagnostic ecosystem engineered for localized MSV classification. The system integrates an optimized MobileNetV2 convolutional neural network architecture trained via transfer learning to classify maize leaves in real time into three categories: Healthy Maize Leaf, MSV Infected Leaf, and Not a Maize Leaf. For smartphone users, the MaizeGuard mobile tier utilizes a React Native and Expo framework integrated with ONNX Runtime for on-device inference and Async Storage for local caching. For users lacking smartphone access, a standalone hardware kit was built using a Raspberry Pi microcomputer, a CSI camera, a 3.5-inch resistive TFT touchscreen, and a custom Python framebuffer application configured via systemd to auto-launch at boot. An optional Node.js/PostgreSQL backend supports asynchronous data synchronization without interfering with core offline diagnoses. The system engineering process was grounded in a systematic review of 24 peer-reviewed studies, which validated lightweight CNN optimization pathways for edge environments and highlighted the scarcity of farmer-ready MSV diagnostic systems. System evaluation encompassed functional validation, multi-platform integration benchmarking, execution latency tracking, thermal profiles, and touchscreen calibration durability under simulated field setups. Experimental results demonstrated that the completed dual-platform ecosystem achieves high classification accuracy, low computational latency, and robust operational resilience under low-resource field conditions. This work contributes a practical, scalable advancement in digital precision agriculture, demonstrating how edge AI design can deliver inclusive diagnostic tools directly to
smallholder farmers in Uganda.
Predictive and prescriptive analytics to strengthen infectious disease surveillance and response
(Uganda Christian University, 2026-06-01) Joy Florence Awor; Judith Anita Namaganda; Calvin Diego Rwomothio
Uganda continues to experience recurring outbreaks of epidemic-prone infectious diseases, driven by persistent gaps in water, sanitation, and hygiene infrastructure, rapid urbanization, population displacement, and a limited capacity for timely detection and coordinated response. Current surveillance systems remain largely reactive and descriptive, focusing on reporting confirmed cases rather than predicting and preventing outbreaks. This study develops a proactive Health Intelligence Platform designed to strengthen Uganda’s epidemic preparedness by integrating predictive and prescriptive analytics with response visualization. Using a curated cholera surveillance dataset as a proof-of-concept, the
platform applies machine learning techniques to forecast potential outbreak hotspots and temporal trends. These predictive outputs are operationalized through an interactive dashboard that provides timely alerts, spatial mapping, risk identification, and decisionsupport indicators for public health officials. The system demonstrates how predictive models combined with environmental factors can enhance situational awareness and support faster, data-driven interventions at both district and national levels. The ensemble models trained on 8,702 cholera surveillance records achieved strong short-term forecasting performance: the Random Forest Regressor attained an R2 of 0.7861 for suspected case prediction and 0.6204 for confirmed case prediction, outperforming XGBoost across all evaluation metrics (MAE and MSE). These accuracy levels represent a meaningful advance over Uganda’s current surveillance infrastructure platforms such as DHIS2, IDSR, and eIDSR which remain descriptive and retrospective, providing no automated outbreak forecasting, no anomaly detection, and no environmental risk integration. The XGBoost-powered 14-day early warning module
further generated forward looking case projections consistent with observed transmission trends, correctly identifying the February 2026 peak of 700 confirmed cases as the most severe anomaly in the 15-year record, with a 200% deviation above the rolling average a signal that the existing national system did not surface proactively. Overall, this project showcases a scalable, Uganda-focused Health Intelligence Platform capable of transitioning the country’s surveillance ecosystem from reactive reporting to anticipatory, data-driven epidemic preparedness.
IoT-enabled smart waste monitoring and predictive route optimization system
(Uganda Christian University, 2026-06-01) Itungo Agaba; Alvin Rubagumya; Mark Calvin Obba
Rapid urbanization in Uganda has led to a significant increase in solid waste generation, while existing collection systems remain largely manual, inefficient and reactive. Municipal waste collection typically relies on fixed schedules without real-time visibility into bin fill levels, resulting in overflow, increased operational costs and environmental risks. This project presents an IoT-enabled Smart Waste Monitoring and Predictive Route Optimization System designed to address these challenges. The system integrates smart bin sensors, real-time telemetry, predictive analytics and route optimization algorithms to enable a data- driven waste collection process. The proposed solution utilizes ultrasonic sensors and Arduino UNO R4 microcontrollers to monitor bin fill levels, combined with machine learning models to forecast waste accumulation patterns. A routing optimization engine based on Vehicle Routing Problem (VRP) techniques
is implemented to generate efficient collection routes. Results from simulation and system testing demonstrated that the proposed system improved operational efficiency through real-time telemetry monitoring and predictive routing. The forecasting models were able to identify high-priority bins before overflow occurred, while the route optimization engine reduced unnecessary collection trips and overall travel distance. System testing further confirmed reliable telemetry transmission, responsive dashboard visualization and effective integration between IoT devices, backend services and machine learning components. The system in general demonstrates the potential to reduce operational costs, prevent
bin overflow and improve urban sanitation. The project contributes a scalable and cost-effective solution tailored for Ugandan municipalities, aligning with Sustainable Development Goals (SDG 11 and SDG 12) on sustainable cities
and responsible resource management.
Bako Analytics: a localized deep learning framework for personalized basketball biometrics and team tactical analysis
(Uganda Christian University, 2026-05-28) Norbert Okidi; Anna Akumu; Zahara Nankya
The advancement of Artificial Intelligence (AI) in sports has largely bypassed low-resource environments in Sub-Saharan Africa due to the prohibitive cost of commercial hardware and the lack of representative datasets for African athletes. This project presents BAKO Analytics, Africa’s first localised multi-model deep learning framework designed for personalised basketball biometrics and tactical diagnostics. The system addresses the gap in accessible sports science in Uganda by implementing a decou- pled client-server architecture comprising a FastAPI-powered backend and a React/Vite web interface. The core innovation lies in a six-model AI pipeline using the YOLO (You Only Look Once) architecture for object detection and pose estimation. Specifically, the framework includes dedicated models for player and ball detection, court keypoint mapping (homography), and a custom biometric engine that decomposes basketball shooting form into four critical phases (DIP, SET, RELEASE, and FINISH) using 17-keypoint skeletal data. Validated through a case study with the UCU Cannons basketball team, BAKO Analytics enables elite-level performance analysis using nothing more than standard smartphone footage and modest computing hardware. Technical evaluation demonstrates high precision in action recognition and spatial tracking, even under the variable lighting conditions of outdoor courts
common in the Ugandan context. Beyond its technical contributions, the project provides a scalable and affordable blueprint for indigenous sports analytics, bridging the technological divide and fostering talent development through data-informed coaching.
Keywords: Basketball Analytics, Computer Vision, Deep Learning, Pose Estimation, Biometrics, Low-Resource AI, Sports Science
Automated coffee bean quality sorting using machine learning and multi-sensor integration: A low-cost solution for Ugandan smallholder farmers
(Uganda Christian University, 2026-06-03) Rebecca Alinda; Benjamin Mutumba Mubeezi; Isaac Nickson Nziriga
Uganda’s coffee sector, which contributes 20–30% of the country’s foreign exchange earnings, faces persistent quality challenges rooted in manual sorting practices. Ninety percent of Uganda’s coffee is produced by smallholder farmers who lack access to affordable sorting technology. Manual sorting is labour-intensive, inconsistent, and prone to human error rates of 20–25% after prolonged operation, limiting farmers’ access to specialty markets that pay 40–60% premium prices. This project presents the design, development, and evaluation of an Automated Coffee Bean Quality Sorting System, which is a low-cost (UGX 450,000–562,500) solution combining multi-sensor integration, machine learning, and embedded systems. The system employs a Raspberry Pi 4 as the central processing unit, a TCS3200 colour sensor for RGB-based bean assessment, a Camera Module 2 for visual inspection, and an Arduino Uno for motor control via an L298N driver. Beans are transported on a conveyor belt and assessed at sequential sensing stations before a servo motor diverts defective beans. The machine learning pipeline employs a dual-model fusion strategy: a Decision Tree classifier trained on colour sensor readings achieves approximately 98% accuracy, while a MobileNetV2 CNN model processed via TensorFlow Lite provides visual classification. A conservative fusion strategy defaults to rejection when model outputs conflict, ensuring food safety. The system achieves a throughput of 1–2 beans per second with inference latency below 50 ms. Key findings include: (1) the Decision Tree outperforms the CNN in the current deployment due to a domain gap between training data and Ugandan Robusta beans under real field conditions; (2) the conservative reject-on-conflict strategy provides a defensible default for food quality systems; (3) the total cost represents a 97–98% reduction versus commercial alternatives costing UGX 18,750,000–56,250,000. The system demonstrates technical feasibility and strong potential for adoption among smallholder cooperatives across East Africa.
Keywords: coffee bean sorting, machine learning, Decision Tree, MobileNetV2, TensorFlow Lite, Raspberry Pi, multi-sensor integration, smallholder agriculture, Uganda, precision agriculture.
Impact of youth unemployment on liveihood among young people in Mukono District
(Uganda Christian University, 2026-06-11) Rebecca Kobusingye
This study investigated the relationship between youth unemployment trends and youth livelihoods within Mukono District, focusing on Mukono Municipality and Gombe Sub-county as contrasting urban and rural case sites. The study was driven by three specific objectives: evaluating local youth underemployment trends, mapping alternative informal livelihood survival strategies, and examining the reach of existing institutional and non-governmental socioeconomic
interventions. Using a mixed-methods research design, quantitative data was gathered from a structured sample of 30 unemployed youth using 5-point Likert scale questionnaires, while qualitative data was captured via 7 Key Informant Interviews (KIIs) with local labor administrators and 7 Focus Group Discussions (FGDs) with youth cohorts. Quantitative data was processed using descriptive statistics (frequencies, percentages, means, and standard deviations) via SPSS Version 26.0, while qualitative text was handled through thematic analysis. The descriptive empirical findings revealed a severe, systemic deficit in formal labor market absorption, evidenced by a high baseline unemployment trend composite mean score. The single highest barrier identified by respondents was structural financial constraints, specifically a lack of initial start-up capital. Methodological evaluation of local livelihood outcomes revealed an inverse relationship between earnings stability and survival tactics; respondents strongly rejected the notion that their daily cash flow was stable enough to anchor basic household needs, driving an overwhelming, absolute reliance on highly volatile informal hustle, such as roadside retail vending and leased boda-boda operations. This high-risk exposure resulted in a broad deterioration of household standards of living. Qualitative triangulation revealed that existing institutional safety nets, including the Parish Development Model (PDM) and local NGO vocational training clinics, are severely limited by low fund pooling and low intake capacities, leaving the vast majority of local youth to navigate
structural poverty independently. The study concludes that youth unemployment in Mukono District is fundamentally a structural crisis of underemployment and capital exclusion rather than a mere lack of labor interest. The study recommends that the Ministry of Gender, Labour and Social Development, alongside the Mukono District Local Government, should restructure youth empowerment funds by removing rigid bureaucratic requirements, expanding low-interest credit access, and shifting focus from short-term theoretical workshops toward long-term subsidization of youth-led informal manufacturing and artisan collectives.
“Socio-economic status of parents on the quality of secondary school children in Namasagali Subcounty in Kamuli District
(Uganda Christian University, 2026-06-02) Ruth Nakityaba
The study examined the socio-economic status of parents on the quality of secondary school children in Namasagali sub county in Kamuli district. This study focused on socio economic status of parents how it affects the quality of secondary education of children whereby it examined the secondary education level in the area of study by analyzing the socio-economic factors and how they predict quality of education, exploring the perceived barriers to high school completion among students from low socio-economic status and also other factors contributing to low and high quality of education in communities of Uganda. The study was carried out using both quantitative and qualitative data. The data was collected using the focused group discussions, interview guides and questionnaires during data collection. Then Yamane’s formula was used for the quantitative data and also nonprobability sampling and clustering and re clustering sampling were used during data collection. The study findings revealed that factors affecting secondary school education include intensive poverty among parents in Namasagali sub county Kamuli district whereby they cannot afford to buy scholastic materials for their children thus school dropout. Some families have large number of children from five and above, yet the source of income is less failing to educate their children especially in secondary schools instead they are taken to gardens for farming. The parental education background also is fundamental factor that affects secondary education where some parents who did not completion high education find it hard to educate their education to complete secondary education and also the still the existing norms and culture among some families where they emphasize boy child education compared to girl child education sending them to marriage hence affecting the secondary school education. The perceived barriers of the study included psychological stress among secondary school children, opportunity cost of school and also gender based problems especially the girls who are less concentrated on when it comes to education compared to boy child. Many secondary school children dropout of school due to lack of scholastic materials thus low completion of secondary school education. As a way of improving the quality of secondary school education among the students in Namasagali sub county in Kamuli district, there are some recommendations for example the government should upgrade and reestablish secondary schools that are easily accessible by students in terms of distance in order to solve the long distance problem thus improving education access, the school mentors should hold school seminars where children are encouraged to focus career up bringing and also the local government leaders should be encouraged to work appropriately through monitoring and evaluating the secondary school progressing order to identify areas that need to be improved for high quality education.
Investigating the effects of early childbearing on the well-being of the young girls in Palorinya Refugee Camp Obongi District
(Uganda Christian University, 2026-06-05) Emmanuel Leju
This study investigated the effects of early childbearing on the well-being of young girls in Palorinya Refugee Settlement. The study was guided by the increasing concern over the high rates of teenage pregnancies and early motherhood among refugee communities, which continue to negatively affect the social, economic, physical, and psychological well-being of young girls. The purpose of the study was to examine the effects of early childbearing on the lives of young girls living in Palorinya Refugee Camp in Obongi District. The study was guided by the following objectives: to identify the causes of early childbearing among young girls in Palorinya Refugee Camp, to assess the effects of early childbearing on their well-being, and to suggest possible interventions for reducing early childbearing and improving the welfare of affected girls. The study employed a descriptive research design using both qualitative and quantitative approaches. Data was collected from young mothers, parents, community leaders, health workers, and humanitarian workers through questionnaires, interviews, and focus group discussions. The collected data was analyzed using descriptive statistics and thematic analysis. The findings of the study revealed that poverty, lack of parental guidance, limited access to education, peer pressure, sexual violence, cultural practices, and inadequate reproductive health information were major contributors to early childbearing among young girls in the refugee settlement. The study further established that early childbearing negatively affected girls’ education, health, emotional stability, social relationships, and economic well-being. Many young mothers experienced school dropout, stigma, depression, health complications, and increased dependency on aid and relatives for survival. The study concluded that early childbearing remains a major social and public health challenge affecting the well-being of young girls in Palorinya Refugee Camp. The study recommended strengthening sexual and reproductive health education, improving access to youth-friendly health services, promoting girl-child education, enhancing parental and community support systems, and implementing programs aimed at empowering adolescent girls economically and socially.
Key words: Early childbearing, well-being, young girls, refugee settlement, adolescent pregnancy, Palorinya Refugee Camp, Obongi District.
Influence of unemployment on drug addiction among youth in Kawempe Division, Kampala District
(Uganda Christian University, 2026-06-02) Aisha Nalubega
This study examined the influence of unemployment on drug addiction among youth in Kawempe Division, Kampala District. The study aimed to establish the relationship between unemployment and drug abuse among youth. A descriptive research design using both qualitative and quantitative approaches was adopted. Data was collected through questionnaires and interviews from selected youth and community leaders. Findings revealed that unemployment contributes greatly to drug addiction due to idleness, stress, peer pressure, and lack of income opportunities. The study also found that drug addiction negatively affects youths’ health, behavior, and productivity. The study concluded that unemployment is a key factor influencing drug addiction among youth in Kawempe Division. It recommended increased job creation, vocational training, and sensitization programs to reduce drug abuse among unemployed youth.
The relationship between exposure to domestic violence and depression among adolescents aged 10–17 years in Kumi District, Uganda
(Uganda Christian University, 2026-06-04) Daniela Amoding
The current study investigated the relationship between the exposure to domestic violence and depression among adolescents from the age group of 10-17 years in Kumi District of Uganda. This study was conceived in the threefold objective: to determine the forms of domestic violence observed in adolescents, the level of depression among adolescents and to investigate the relationship between domestic violence and depression. A cross-sectional quantitative design was used in the research. 40 respondents were chosen using simple random sampling. Structured questionnaires were used to collect data which included a modified depression scale from the Patient Health Questionnaire (PHQ-9). Data were analyzed by descriptive statistics, including frequencies and percentages, and inferential statistics including the Chi-square test. They found that adolescents were also exposed to many forms of domestic acts of violence, especially verbal and emotional abuse. Most of the adolescents also experienced moderate to high levels of depression. In addition, it was also established that exposure to domestic violence was significantly associated with depression. The research concludes that domestic violence is a leading contributing factor to depression in adolescents in Kumi District. The research suggests interventions at the family, school and community levels to address these needs. This paper recommends, among other things, helping to address domestic violence and promote adolescent mental health.
Teenage pregnancy and the education of the girl child in Uganda: a case study of Kira Village, Kampala District
(Uganda Christian University, 2026-06-02) Gladys Nakachwa
The study examined the impact of teenage pregnancy on the education of the girl child in Kira Village. The study was guided by three objectives: to identify the social, economic, and institutional factors contributing to teenage pregnancy among school-going girls; to assess the effects of teenage pregnancy on educational outcomes such as enrolment, attendance, academic performance, and dropout rates; and to propose evidence-based recommendations for improving support systems for affected girls. The study adopted a cross-sectional research design using both qualitative and quantitative approaches. A sample size of 80 respondents was selected from a target population of 100 participants, including adolescent girls, parents, teachers, health workers, police officers, and representatives from community organizations. Data were collected using questionnaires and interviews, while analysis involved descriptive statistics and thematic interpretation. The findings revealed that teenage pregnancy in Kira Village is largely influenced by social stigma, poverty, cultural beliefs, inadequate educational support systems, and weak school policies. The study further established that teenage pregnancy negatively affects girls’ education through reduced school enrolment, poor attendance, low academic performance, and increased dropout rates. Many pregnant girls experienced discrimination, financial hardships, and lack of institutional support, which limited their chances of continuing education. The findings also indicated that schools lacked clear re-entry policies and sufficient mechanisms to support adolescent mothers. The study concluded that teenage pregnancy remains a major barrier to the educational attainment of girls in Kira Village and contributes to cycles of poverty, inequality, and social exclusion. The study recommends the establishment of mentorship programs, stronger community awareness campaigns, financial support initiatives, implementation of clear school re-entry policies, and enhanced partnerships among schools, healthcare providers, government institutions, and NGOs. The study further recommends strengthening child protection systems and expanding comprehensive sexuality education to reduce teenage pregnancy and promote educational continuity among girls.
Effects of child abuse on the mental health of children in Kamuda Sub-county, Soroti District
(Uganda Christian University, 2026-06-02) Julius Opio
This chapter include the background to the study, problem statement, research purpose, objectives, research questions, scope, justification, significance, conceptual framework and operational definitions. The study aims to examine the effects of child abuse on the mental health of children in Kamuda Sub county, Soroti District.
The influence of single parenting on the academic performance of university student
(Uganda Christian University, 2026-06-02) Sonia Kebirungi
This study examined the influence of single parenting on the academic performance of university students at Uganda Christian University, Mukono Campus. The study was guided by three objectives: to examine students' perceptions of single parenting and academic performance, to identify the challenges faced by students from single-parent households, and to explore the coping strategies they use to achieve academic success. A qualitative research approach was used, and data were collected through interviews with 20 undergraduate students who were raised in single-parent families. The data were analyzed using thematic analysis to identify common experiences and views among the participants.
The findings revealed that students held different perceptions about single parenting. While some viewed it as a challenge due to financial difficulties, emotional stress, and limited parental support, others considered it a source of motivation and personal growth. The study also found that financial constraints, emotional challenges, and social stigma were the major factors affecting students' academic performance. Despite these difficulties, students adopted coping strategies such as effective time management, participation in group discussions, seeking counselling, relying on social support, and maintaining a positive attitude. The study concludes that single parenting does not automatically lead to poor academic performance, as academic success depends on the support available to students and their ability to overcome challenges. The study recommends strengthening counselling services and support systems for students from single-parent households.
The impact of domestic violence on the mental health of children in Arua Central Division, Arua district
(Uganda Christian University, 2026-06-01) Jane Keji Joseph
The presence of domestic violence within the family may cause many mental issues among kids in the future. There are many studies indicating that domestic violence affects children's lives by increasing their risks of developing various mental diseases. For example, some research has shown that children who were victims of domestic violence are more likely to suffer from anxiety and depression than other children. Nevertheless, not all of the kids subjected to violence have bad consequences. The current paper is focused on reviewing the literature related to the different types of violence that children experience and the impact of domestic violence on the mental health of children. It is no doubt an upsetting experience for the children whose families suffer from domestic violence, which is a traumatic and ongoing experience. These experiences can accumulate through time and affect all aspects of life, including health and wellbeing of children. According to parent report, almost 4% of children had been witnesses of severe domestic violence. The factors related to greater likelihood of child witnesses of domestic violence were older age, mixed ethnicity, physical disorders, more than one child in the family, divorced parents, renting the house, living in poor neighborhoods, mother's emotional status, and dysfunctional family Being witnesses of domestic violence does not automatically imply that they observe violence happening because many of them relate events that they may have heard but not
Women's participation in the Parish Development Model (PDM) implementation: Challenges and opportunities in Kyabakadde Parish, Mukono District
(Uganda Christian University, 2026-06-01) Alice Nansubuga
The Parish Development Model (PDM) is one of the key government programs introduced in Uganda in 2021. It aims at facilitating the transition of 39% of Ugandan households on subsistence farming into the money economy via entrepreneurship development, financial inclusion, and community planning. The government allocated 30% of the Parish Revolving Fund (PRF) to women, indicating the formal recognition of them role within the program. Nonetheless, this study demonstrates that in Kyabakadde Parish in Kyampisi Sub-county, Mukono District, women's participation in the PDM lags behind its intended impact. Based on the qualitative descriptive cross-sectional approach, the data collection involved 17 participants, among which there were women who benefited from the program, local council leaders, and a Community Development Officer. Data were collected using individual in-depth interviews and focus groups discussions in the period between March and early March 2026. The collected data were subjected to analysis based on thematic coding relative to the research objectives. The study findings indicate that a significant number of women in Kyabakadde parish have not benefited from the PDM funds despite completing the registration process, while those who have received funds have received an amount ranging from UGX 700,000 to 800,000 instead of the intended UGX 1,000,000 for each group of beneficiaries. The level of awareness of the PDM among women is relatively low, as most only had an understanding of the financial aspect of the programme, having no idea about planning and enterprise development processes. Though women participated in PDM meetings, their role was confined to being mere spectators, as their participation in decision-making was hindered by several factors, including lack of confidence, cultural constraints, and the process of organizing the meetings, which favoured men's voices. Conclusions drawn from the research indicate that although the PDM has provided platforms that support economic empowerment among women, there seems to be little understanding of the processes due to systemic problems and continued social injustices. Recommendations target the Government of Uganda, local leaders, NGOs, communities, and researchers in the future. The areas of concern include increased sensitisation of the 2 community, increased transparency in the allocation of funds, increased self-confidence among women, financial literacy, and good governance within the PDM.
The response of international bodies to human rights violations and impunity in Kampala Uganda during electoral campaigns
(Uganda Christian University, 2026-06-12) Alexia Nambache
This paper has explored how the international organizations have responded to violation of human rights and impunity in electoral campaigns in Kampala, Uganda. The objective of the study was to investigate the manner in which international organizations react to the abuse of human rights during campaigns, the factors which limit their effectiveness, the effects of impunity on the safety of human rights, and the prevalent types of abuses that are witnessed during campaigns. The research problem was based on what was found to be the gap between the commitment of the government of Uganda to the international human rights standards and the prevalence of widespread human rights violations and impunity during electoral campaigns despite international attention and intervention. The research design adopted was a qualitative research design and was conducted in Kampala City, in areas that are often related to political activities and election related tensions. Semi-structured interviews with key informants, review of the documentary, and analysis of the case study, were used as data collection methods. The results have shown that the electoral campaigns in Kampala have always been marked by a high level of violation of human rights involving arbitrary arrests, intimidation, excessive use of force, suppression of the freedoms of assembly and expression, harassment of journalists, and targeting of the opposition supporters. The research also determined that such abuses are systemic and are often enabled by the selective enforcing of laws and politicization of state institutions. Despite the active efforts of international bodies including the United Nations, African Union and international non-governmental organizations to monitor, document and condemn these abuses, their efforts have not been very effective in holding them accountable.
Mental health service delivery and the wellbeing of adolescents in Ki- Mombasa Zone Bwaise Division
(Uganda Christian University, 2026-07-04) Martha Anita Nakitende
The study examined mental health service delivery and the wellbeing of adolescents in Ki-Mombasa zone Bwaise division; the study identified specific aspects that may determine the availability, accessibility and the social work interventions towards mental health services to the adolescents. This study was guided by objectives which included, to assess the availability of mental health services, to examine the extent of mental health service accessibility and to explore the social work strategies aimed at improving provision of needed mental health services in Ki-Mombasa zone, Bwaise division. The findings indicate that mental health services in Ki-Mombasa zone are inadequate, with limited access to specialized care, lack of trained health care providers, and insufficient funding. The study also reveals that adolescents in the area experience high levels of mental health problems, including depression, anxiety
and substance abuse. The study concludes that there is need for urgent attention to address the mental health needs of adolescents in Ki-Mombasa zone. Recommendations include addressing social cultural barriers, ample training of community-based leaders and call for social workers at Uganda Christian University to conduct more research and also educate others about adolescent mental health thus promoting community-based
initiatives to support adolescent wellbeing.