Meru University of Science and Technology

This meta-analytic study aims to comprehensively examine the relationship between mental health, learning capacity among health education students, and competencies in nosocomial disease risk management through cross-contextual empirical synthesis. An analysis of 47 studies involving 12,847 participants from 15 countries demonstrates a strong, statistically significant association between students' mental health and competencies in nosocomial infection prevention, as reflected by a correlation coefficient of r=0.68 (p<0.001) and a 95% confidence interval of 0.61-0.74. Students with high mental health scores (M=78.4; SD=8.2) exhibited substantially superior understanding of infection prevention protocols, namely 43% higher than the control group (M=54.7; SD=12.1; t(846)=18.42; p<0.001; d=2.31). Structural equation modeling confirmed learning capacity as a significant partial mediator (β=0.52; p<0.001), with an indirect effect reaching 35.4% and a 95% CI range of 28.6-42.1%. Mindfulness-based psychoeducational interventions were shown to enhance nosocomial risk identification abilities by 38.7% (F(2,564)=42.18; p<0.001; η²=0.41) while reducing clinical anxiety by 31.2% (t(382)=9.84; p<0.001). These findings extend the frameworks proposed by Song (2024) and Schutte et al. (2025), which primarily emphasize cognitive aspects, by demonstrating that the integration of psychological dimensions yields a multidimensional predictive model explaining 64.3% of the variance in risk management competence (R²=0.643; F(5,841)=304.76; p<0.001), surpassing conventional models that account for only 38-45% of the variance.

This meta-analysis systematically examines the psychological effects of the Gaza blockade on the mental health of affected populations, synthesizing data from 47 studies with 31,842 participants published between 2007 and 2024. The results show a significant increase in mental health disorders, with a pooled effect size of r = 0.73 (95% CI [0.68, 0.78]). PTSD was the most prevalent at 73.2%, followed by depression at 68.7%, and anxiety at 65.4%, highlighting the severe psychological impact. Hierarchical regression analysis revealed a strong correlation between the duration of the blockade and the severity of psychological symptoms (β = 0.68, p < .001). Children and adolescents were particularly vulnerable, with an odds ratio of 2.84 for developing complex trauma. Structural equation modeling indicated that limited access to mental health services significantly worsened psychological conditions, with model fit indices (χ2/df = 2.34, CFI = 0.96, RMSEA = 0.058). This study also identified a higher prevalence of disorders compared to previous studies and uncovered a new pattern of intergenerational trauma (d = 0.82). This meta-analysis enhances the understanding of how prolonged blockades affect mental health and provides a foundation for evidence-based psychosocial interventions.

The Sustainable Development Goal agenda 6.2 targets to achieve universal basic sanitation and hygiene by 2030 (United Nations, 2018). This paper assessed the influence of social cultural factors on adoption of sanitation systems in rural communities. The article was based on field research that employed convergent research design where both qualitative and quantitative data was gathered simultaneously. Quantitative data was gathered using mixed questionnaires from 388 household heads selected using stratified proportionate simple random sampling techniques.The study objectives were to assess theinfluence of religion, traditions and gender on adoption of sanitation systems in rural communities and to assess the influence of toilets on adoption of sanitation systems in rural communities Qualitative data was collected using an interview guide from a purposively selected focus group consisting of 13 participants. The findings were organized into themes and presented in narratives. Quantitative datawas analyzed using the statistical package for social sciences (SPSS) version 25.0 which generated descriptive and inferential statistics to unveil the relationship between variables. The findings, established that the adoption of unimproved toilets was the main form of sanitation system, covering a mean of 2. 9807.A unit increase in traditions led to a 0.029 decrease in adoption of sanitation systems (p.value=0.009) in the rural communities. A unit increase in gender roles led to a 0.142 increase in adoption of sanitation practices (p.value=0.001). Residents believed that faeces left in the open could be used for witchcraft purposes, a tradition that had a positive impact in eradication open defecation. Some religious denominations like the Kavonikia and Agendi associated diarrhea with demons other than the poor sanitation which was seen to facilitate poor sanitation practices and systems. The study recommended that the Community Led Total Sanitation (CLTS) strategy should target both open defecation and enlighten residents on the dangers of adopting unimproved latrines. The study also recommended that inclusion of women in household sanitation matters to ensure adoption of women and children friendly household sanitation facilities and incorporation of religious leaders as advocates of sanitation systems alongside demographic, psychosocial and environmental factors.

Bochdalek hernia (BH) is a rare paediatric condition, but late diagnoses occur in adults. It occurs following incomplete posterolateral development of the diaphragm. Adult BH is asymptomatic in most cases, but it has the potential of being life-threatening. This case report is of a 35-year-old female patient who presented to us with a history of childhood abdominal discomfort leading to frequent hospitalisation. Presently, she came with complaints of increasing abdominal pain, postprandial vomiting, and a 31% weight loss over 3 months before the current admission to our facility. A preoperative diagnosis of a Type IV hiatus hernia with a possible volvulus was made with the assistance of radiology, which turned out to be left BH intraoperatively. Only the herniated stomach was laparoscopically reduced due to technical difficulties and pexied along the defect to seal it. The postoperative period was unremarkable. This case report highlights the need for maintaining a high index of suspicion and surgical preparedness for BH in patients with high-grade hiatus hernia.

This paper explores the disconnect between policy rhetoric and implementation at the intersection of sanitation equity and climate change in Nairobi, Kenya. To examine the current sanitation adaptation trajectory, we reviewed Nairobi’s sanitation policies, planning, and investment frameworks, focusing on their integration with climate adaptation strategies and consideration of equity in terms of distribution, recognition and processes. We used a socio-technical regime framework to map the current sanitation service configurations in Nairobi and projected their future under different climate change scenarios. Our findings provide evidence for a disconnection between policy rhetoric and implementation, prioritising sewerage development at the expense of other sanitation regimes. Despite recognising equity issues in policy, substantive action towards equitable sanitation governance is lacking. This imbalance hinders the realisation of the constitutionally recognised right to adequate sanitation in the foreseeable future. The anticipated impact of climate change on Nairobi’s sanitation sector suggests an exacerbation of existing service inequalities. Our projection indicates that by 2030, a sizeable portion of Nairobi’s residents will experience poor sanitation services. Our study emphasizes the critical need for a fundamental paradigm shift. It calls for a robust and honest discussion on delivering high-quality, resilient sanitation services at scale including both sewer and non-sewered sanitation and necessitating substantial public investment and support for all systems. This reappraisal is imperative for ensuring equitable and sustainable sanitation solutions in the face of climate change.

Deep convolutional neural networks (CNNs) are a popular choice for many image classification tasks due to their good performance. In general, there is a correlation between the generalization performance of a deep learning model, the number of training samples available, and the number of free parameters in the model. If there are too many free parameters in the model relative to the amount of training data, the model overfits the training data. Reducing the number of trainable parameters in CNNs while maintaining performance levels is an active area of research. This is important, especially in resource-constrained environments where either the size of the training data is insufficient or the memory of the hardware where the model is to be deployed is limited, such as in mobile vision applications. In this paper, kernel transposition is proposed as a method for the reduction of free parameters in the model’s convolutional layers. This allows kernel reuse, in which a model learns a given kernel only once but uses it twice. The learned kernels and their transposes can either be used in sequence or in parallel to one another. These form series and parallel convolutional modules respectively. The modules are used as replacements of the traditional convolutional layers in a CNN model. The use of the modules reduces the number of free parameters and computational costs associated with convolution operations in the model without necessarily compromising its generalization performance. The proposed method is generic and can be adapted to existing state-of-the-art network architectures as demonstrated in the experiments using established architectures. The proposed method was validated on the CIFAR-10 and CIFAR-100 standard datasets using a series of experiments with five model architectures: a basic 5-convolutional layer CNN model, the ResNet-56, small MobileNetV3, EfficientNetB0 and ConvNext model architectures; all trained from scratch. Models based on standard convolutional layers (for basic CNN and ResNet-56 architectures) and depth-wise convolutional layers (for MobileNetV3, EfficientNetB0 and ConvNext architectures) were compared against those based on the proposed convolutional modules. The models based on the standard and depth-wise convolutional layers were used as baselines. The comparisons were in terms of the number of parameters, computational efficiencies (Floating Point Operations / FLOPs) and classification accuracies of the models. For a kernel size k, the experiments showed that, despite improvements on parameter and computational efficiencies, models based on the proposed modules for the basic CNN (k = 3) architecture had lower accuracy levels compared to their baseline model. However, the huge saving on the number of parameters (> 88%) and FLOPs (> 84%) is significant compared to the drop in accuracy (< 4%). For the MobileNetV3 (k = 3 or k = 5) and EfficientNetB0 (k = 3 or k = 5), only the model based on the series module had lower accuracy than the baseline models. The models based on the parallel module were generally at par (within a margin of $$\pm$$ 1.5%) in terms of accuracy with their respective baselines. In relation to the ConvNext (k = 7) and the ResNet-56 (k = 3 or k = 11, or k = 19) architectures, other than the improved parameter and computational efficiencies, the classification accuracies of all the models based on the proposed modules were either at par or better than those of their baseline models. In all the above architectures, the use of the proposed modules reduced the number of convolutional parameters in the networks by at least 78% with fewer FLOPs relative to the baseline models

Rapid urbanization in the developing world has exacerbated the crisis of poor sanitation within sprawling informal settlements. Container-Based Sanitation (CBS) has emerged as a promising, alternative solution designed to fill these critical infrastructural deficits where conventional sewage systems are absent. Despite its apparent potential, there is a recognized gap in the contextual understanding of CBS implementation, particularly concerning the influence of local social and political dynamics, which ultimately hinders effective scale and sustainable programming. This qualitative study aimed to explore the intricate factors surrounding the implementation of CBS in an urban slum, specifically investigating the technology’s operational roles, the influence of existing power structures, the key challenges encountered, and viable strategies for enhancing its adoption within an informal settlement in Nairobi, Kenya. Data collection utilized in-depth interviews (IDIs) with 25 key participants, including residents, local community leaders, and relevant government officials working in the Mukuru Kwa Reuben informal settlement. A purposive sampling method guided participant selection, and the collected qualitative data was rigorously analyzed using a framework analysis approach. The study’s findings indicate that CBS was perceived positively as a cost-effective and safe solution for improving household sanitation. However, its widespread scalability was significantly limited by a series of entrenched contextual challenges. The challenges stemmed from a inadequate governance, power structures and coordination of governance and coordination, including inaccurate data and failed consultation, which limited project success. Financially, the sector was underfunded and deprioritized, leading to an inequitable, high-cost user-fee model that burdened residents. A consistent lack of community involvement undermined the long-term sustainability and adoption of the interventions. The research concludes that scaling CBS successfully requires transcending purely technical considerations. The findings will contribute to policy-making in Nairobi’s Special Planning Area framework and broader debates on urban sanitation financing. Future implementation strategies should focus on establishing collaborative governance models and integrating local leadership to navigate and mitigate existing power dynamics, thereby addressing the systemic, contextual barriers required for achieving sustainable and equitable sanitation coverage.

Abstract Purpose: Breast cancer is one of the commonest cancers globally as well as one of the leading causes of mortality. In Kenya, breast cancer accounted for 16.2% of all new cases and was the 2nd leading cause of mortality, accounting for 11.6% of cancer deaths in 2022. Delayed diagnosis of breast cancer significantly impacts treatment outcomes and survival rates. In Kenya, a high proportion of breast cancer patients present with advanced-stage disease, yet the factors contributing to this delay are not well understood. Methods: A mixed-method cross-sectional study was conducted at Meru Teaching and Referral Hospital (MeTRH) between August and October 2024. Data collection commenced with a retrospective review of 397 patient records (2015–June 2024), interviews with 73 breast cancer patients, surveys of 54 healthcare professionals, and key informant interviews with 7 heads of departments. Delayed diagnosis was defined as presentation at Stage III or IV and early diagnosis stages I and II. Quantitative data was analyzed using descriptive statistics and logistic regression, while qualitative data underwent thematic analysis. Results: The prevalence of delayed breast cancer diagnosis was 67.4% (95% CI: 62.7–72.1) and a mean age of 51±14, and the vast majority of patients were female (98.2%), with only a small fraction of male cases (1.8%). Most patients had invasive ductal carcinoma (95.2%), with 43.8% having Grade II and 44.9% having Grade III tumors. Rural residence (aOR: 3.82, 95% CI: 2.14–6.81), financial constraints (aOR: 3.45, 95% CI: 1.92–6.19), and distance &amp;gt;5 km to healthcare facilities (aOR: 3.28, 95% CI: 1.84– 5.83) were strongly associated with delayed diagnosis. Professional barriers included limited advanced knowledge of breast cancer (18.5%), inadequate training (68.5%), and diagnostic challenges (66.7%). Health facility factors included frequent equipment breakdowns, the absence of a dedicated breast clinic, and limited specialist availability. Patient-related factors included accidental symptom discovery (54.8%), financial barriers (80.8%), and cultural misconceptions (63%). Conclusion: Delayed breast cancer diagnosis at MeTRH results from a complex interplay of socioeconomic, professional, and systemic barriers. Interventions should focus on strengthening and improving healthcare access for rural populations, strengthening professional capacity building, and addressing health literacy and financial barriers to care. Citation Format: Denis Munene, Alfred Owino, Joseph Muchiri, Stephen Chege. Determinants of Delayed Breast Cancer Diagnosis Among Patients in Meru Teaching and Referral Hospital, Kenya [abstract]. In: Proceedings of the 13th Annual Symposium on Global Cancer Research; 2025 Sep 16. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2025;34(12_Suppl):Abstract nr 12.