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Recent Submissions

Towards Critical Literacy Approaches in Access to Information Resources by Students in Selected University Libraries in Kenya.
(UoEm, 2025-11-25) Mutegi, James Njue
The future of university libraries relies on Critical Information Literacy (CIL) to empower students, challenge traditional systems, and ensure fair access to knowledge. CIL interrogates the effectiveness of Information Literacy (IL), disrupts inequitable systems, and creates student-driven training. Further, it supports inclusive and ethical publishing models while rejecting the notion of libraries as neutral spaces. CIL and Metaliteracy are interdependent. Metaliteracy is a form of user education that promotes critical thinking and collaboration in a digital age, providing a comprehensive framework to effectively participate in social media and online communities. This study aimed to investigate CIL approaches and how students can improve access to information in selected university libraries and provide a framework for the same. The objectives of this study were to: examine the types of CIL information resources that the selected university libraries offer; analyze the methods used by librarians in implementing CIL; explore the factors affecting the implementation of CIL in selected university libraries, and propose a framework to enhance CIL in selected university libraries in Kenya. The Social Cognitive Theory (SCT) and Critical Research Paradigm (CRP) provided a theoretical framework for this study. The research design adopted for this study was a convergent parallel mixed method containing both qualitative and quantitative approaches. The study targeted a population of 473 respondents comprising 431 undergraduate and postgraduate students, 28 ICT/Reference Librarians, and 14 University Librarians. Based on the inclusion and exclusion criteria, the final population consisted of 337 undergraduate students, 94 postgraduate students, 28 ICT/Reference Librarians, and 14 University Librarians. A census approach was adopted to involve all 473 eligible respondents in the study. Quantitative and qualitative data were collected using questionnaires and key informant interviews. Quantitative data was analyzed using SPSS, and qualitative data was collected using Atlas. ti. The study findings established that the selected university libraries offered a range of CIL resources for students, with library OPAC and LCC leading and shelf signage being the least offered CIL resources. Findings also revealed that the selected university libraries offered various CIL methods, with library orientation leading and feminist pedagogy being the least offered. Among the challenges were a lack of trained librarians to teach CIL, power dynamics, inadequate ICT infrastructure, and a lack of interest in learning CIL. The study concluded that several challenges confronted CIL resources and delivery methods, and that students lacked self-efficacy in the use of CIL resources and methods of teaching. The study recommended developing national guidelines on CIL approaches and drawing principles for implementing CIL. The other recommendations of the study were awareness creation on CIL resources, enhancement of CIL resources, enhancing students’ metaliteracy skills, faculty and librarian collaboration, developing CIL Curriculum, establishing a feedback mechanism, and providing librarian support. Keywords: Access, Critical Information Literacy, Metaliteracy, Critical Librarianship, Librarian power, University library, University Librarian
The Moderating Role of Institutional Policy Implementation on Quality of Work Life - Employee Performance Nexus: Evidences from Public and Private Hospitals in Kenya
(University of Embu, 2025) Mabele, Trinner Mukamba; Kinyua, Jesse Maina; Bengat, Joseph
This study purposed to determine the moderating role of Institutional Policy implementation on the relationship between quality of work life and employee Performance in Public and private hospitals in Kenya. Correlation and cross-sectional descriptive research designs were adopted. Primary data were collected, using structured questionnaires, from 370 hospital employees, drawn from 2 level 6 and 10 level 5 hospitals in Kenya. Descriptive and inferential statistics were used to analyze data and hypothesis was tested at 5 percent level of significance. The study findings revealed that Institutional policy implementation positively and significantly moderates the quality of work life -employee performance nexus in both public and private levels 5 and 6 hospitals in Kenya. It was therefore concluded that improvement in Institutional policy implementation on medical supply and training significantly increases the positive influence that quality of work life has on driving employee performance in Kenyan hospitals. The study recommends the hospitals in Kenya to effectively implement favorable hospital policies in order to enhance their employee performance. The study results make substantial contributions to the existing literature on moderating effect of institutional policy implementation and offer valuable input in policy formulation in hospitals.
Exploring Genetic Diversity: Optimizing Simple Sequence Repeat (SSR) Markers in Crotalaria for Enhanced Precision in Biodiversity Research
(University of Embu, 2025-05-14) Odhoch Phenny Sharon; Budambula Nancy L. M.; Felix Kiprotich; Muli Joshua Kiilu
Crotalaria is a plant genus with more than 700 species of shrubs and herbs. Despite its potential economic importance, Crotalaria has received limited research attention; hence, there is limited information on its genetic diversity. Hence, there is need to establish its genetic diversity as a foundation for its conservation and breeding. The current study aimed to optimize and validate simple sequence repeat (SSR) markers polymerase chain reaction—conditions for the assessment of genetic diversity in Crotalaria. The genomic DNA of 31 Crotalaria accessions was extracted from 2-week-old leaves using a modified CTAB protocol and Quick-DNA Plant/Seed Kits (Zymo Research Corp) were used for recalcitrant samples. The samples were then amplified using the 29 SSR markers under the optimized conditions. The polymorphism information content (PIC) of the polymorphic markers was calculated to determine their effectiveness. This study determined that the optimal concentrations of dNTPs, MgCl2, and primers as 2.5, 2, and 5 mM, respectively, and the quantity of the DNA template was 1 μL, and the quantity of Taq was 0.125 μL in a 25 μL reaction mixture. The mean PIC value was 0.233, which shows that the markers were slightly informative. The marker PC004 was the most informative marker with the highest PIC value (0.605) and it detected the largest number of alleles despite being a hexanucleotide motif repeat. Its uniqueness augments its potential use in the assessment of genetic diversity. This study implies that the SSR markers designed and optimized for the study are significant for genetic diversity and population structure analysis of Crotalaria species and molecular verification of Crotalaria genotypes as well as other related genera. Besides, the results of the study form a basis for genetic improvement of Crotalaria.
Sentiment Analysis-Based Model for Monitoring UserEngagement With Mental Health Chatbots
(University of Embu, 2025) Mmbayi, Ian Igado; Gakii, Consolata; Musyoka, Faith Mueni
Mental health challenges, particularly among youth, are compounded by stigma and limited access to professional care. Thishas driven demand for scalable digital solutions like chatbots. This study introduces a sentiment analysis-based model toassess user satisfaction with mental health chatbots, analyzing 82 102 reviews from six popular apps on Google Play andApple’s App Stores. A multi-class sentiment classification of positive, negative, and neutral was implemented, enhanced bySynthetic Minority Over-sampling Technique for class balancing, comparing five traditional machine learning models with Bidi-rectional Encoder Representations from Transformers, a transformer model. Random Forest achieved 98.18% accuracy amongtraditional models, while BERT outperformed all with 99.17% accuracy, surpassing prior benchmarks. Aspect-based analysisrevealed that Emotion and Usability drive positive feedback, while Reliability issues fuel negative sentiments, offering action-able insights for developers to enhance chatbot design. This work advances digital mental health research by integratingmulti-class classification, transformer models, and aspect-based analysis, demonstrating a scalable framework for evaluating userfeedback.
Forecasting Kenya's public debt using time series analysis
(University of Embu, 2024-08-03) Obwoge Frankline Keraro; Zakayo Ndiku Morris; Kitavi Dominic Makaa; Maurice Wanyony
Accurately forecasting public debt is essential for developing countries like Kenya to maintain fiscal sustainability and economic stability. This study aimed to identify the best time series forecasting model for predicting Kenya's future public debt to help policymakers create effective fiscal reforms. The Autoregressive Integrated Moving Average (ARIMA) and Holt-Winters exponential smoothing models were tested due to their ability to handle complex patterns and seasonality in time series data. Public debt data from Kenya from 2001 to 2021 were analyzed, and both models were applied to the processed data. The ARIMA (0,2,1) model, which uses second-order differencing and a moving average component, was found to be the best model based on information criteria. The Holt-Winters additive method also showed good performance, adapting well to recent data and seasonal trends with optimized smoothing parameters. Both models produced forecasts that closely matched the actual debt figures for 2022 and 2023, with an error margin of only 0.73. Measures of accuracy, such as Mean Absolute Percentage Error (MAPE) and Mean Absolute Scaled Error (MASE), confirmed the reliability of the models, with ARIMA performing slightly better than Holt-Winters. While previous studies have looked at debt forecasting for Kenya, this research offers a thorough evaluation and comparison of two strong time series models. Unlike existing literature, this study provides a rigorous out-of-sample forecasting assessment, identifying the best approach for reliably predicting Kenya's debt. However, the study is limited by its focus on univariate time series models, which could be improved by including relevant external economic variables. The findings show that the ARIMA and Holt-Winters models are accurate tools for forecasting Kenya's public debt, helping policymakers to develop sustainable debt management strategies and fiscal reforms based on reliable future projections.