Eden University, formerly known as Eden Institute, is a private institution of higher education located in Lusaka Zambia.It is affiliated with the Seventh-day Adventist Church and has made notable humanitarian contributions to the less privileged in Zambia.Established in 2010 as a teachers' training institute, Eden University has, over the years, evolved into a large university that boasts of being the first Zambian institution to offer a bachelor's degree in Fire Engineering.In 2018, the university organized an event that was supposed to feature PLO Lumumba and talk about Chinese influence on Africa, but due to the controversial nature of the topic, the then PF government which was widely seen as Pro-China denied him entry into the country. However, in 2021 when the UPND government took over after defeating the PF regime in elections that same year, PLO Lumumba was allowed into the country and the event was finally held on 26 September 2021. .
BackgroundThe rapid emergence of generative artificial intelligence (AI) tools such as ChatGPT is transforming teaching and learning practices in higher education. This study assessed the awareness, attitudes, and usage patterns of ChatGPT among university students in Zambia and examined factors associated with students’ attitudes toward the technology.MethodsA multi-institutional cross-sectional study was conducted among 1,829 university students in Zambia using a structured questionnaire adapted from instruments informed by the Technology Acceptance Model (TAM). Data were analysed using SPSS version 26.0, with statistical significance set at p < 0.05.ResultsAmong the 1,829 participants, 81.7% were aged 18–25 years, 52.5% were female, and 92.4% were unmarried. Overall, 96.8% of the students had heard of ChatGPT, and 85.6% reported having used it before this study. Among AI-usage constructs, 74.8% of respondents perceived high risks associated with AI use, 73.3% perceived ChatGPT as easy to use, 64.0% perceived it as useful, and 56.8% reported behavioural intention to use AI tools. In multivariable analysis, students aged ≥40 years were more likely to report positive attitudes toward ChatGPT compared with those aged 18–25 years (aOR = 5.91; 95% CI: 1.23–28.33; p = 0.026). Technology/social influence was also significantly associated with positive attitudes (aOR = 2.04; 95% CI: 1.55–2.68; p < 0.001). Conversely, perceived risks were associated with lower odds of positive attitudes (aOR = 0.57; 95% CI: 0.43–0.75; p < 0.001). Regarding ChatGPT use, perceived usefulness significantly predicted higher usage (aOR = 1.49; 95% CI: 1.17–1.89; p = 0.001), while perceived risks were associated with reduced usage (aOR = 0.61; 95% CI: 0.45–0.84; p = 0.003).ConclusionAwareness and use of ChatGPT are widespread among university students in Zambia, with more than four out of five students reporting prior use. Perceived usefulness and social influence were positively associated with adoption, whereas perceived risks were linked to lower attitudes and reduced use. These findings highlight the need for higher education institutions to develop clear policies and integrate AI literacy into curricula to promote responsible and effective use of generative AI technologies in academic environments.
Abstract Background: Healthcare-associated infections (HAIs) remain a major global public health challenge, with a disproportionately higher burden in low- and middle-income countries (LMICs), including Zambia, where infection prevention and control (IPC) systems face resource and capacity constraints. Effective IPC programs are essential for reducing HAIs, limiting the emergence and spread of antimicrobial resistance (AMR), and improving patient safety. This study assessed the implementation status of IPC programs in Zambian hospitals. Materials and methods: A descriptive multicenter cross-sectional survey was conducted from June 1–30, 2025, in 18 public hospitals across Zambia. The World Health Organization Infection Prevention and Control Assessment Framework (IPCAF) tool was used to evaluate IPC implementation across eight core components. Descriptive statistics and Welch’s independent-samples t-test were used to compare IPC implementation levels across hospitals. Results: The overall mean IPC score was 558 out of 800, indicating an intermediate level of implementation. Half of the hospitals (50%, n = 9) achieved advanced implementation, 39% (n = 7) intermediate, and 11% (n = 2) basic levels. Tertiary hospitals performed better than secondary hospitals (mean score 622 vs 507). The highest-scoring components were IPC programs (86%) and guidelines (88%), while the lowest were workload, staffing, and bed occupancy (60%) and IPC education and training (63%). Despite established IPC committees and guidelines, gaps remained in staff training, HAI and AMR surveillance, and infrastructure. Conclusion: Most Zambian hospitals have achieved intermediate to advanced IPC implementation. However, sustained investment in workforce capacity, surveillance systems, and infrastructure is required to strengthen IPC practices and support national AMR containment efforts.
Machine learning models can improve insurance pricing accuracy but create challenges for actuarial transparency and model governance. This paper develops a SHAP-based governance framework for evaluating explainable machine learning in motor insurance pricing, with particular attention to ASOP 41 and ASOP 56. The framework is evaluated using the real freMTPL2freq portfolio of 677,991 French motor third-party-liability policies. A Poisson GLM and gradient-boosted Poisson model are compared using out-of-sample deviance, cross-validation, calibration, and Gini discrimination measures. The gradient-boosted model achieves a 2.2–2.4% reduction in Poisson deviance relative to the GLM and substantially higher risk-ranking discrimination (Gini 0.158 versus 0.089). SHAP analysis identifies bonus-malus level, driver age, and population density as important predictors and indicates an age-by-vehicle-power interaction. A geographic proxy-discrimination audit finds material variation in predicted risk across areas and regions, while residual association with population density is weak after controlling for conventional rating factors. The results demonstrate that SHAP can provide useful evidence for model understanding and actuarial communication, but cannot by itself establish compliance with ASOP 41 or ASOP 56. The proposed framework therefore integrates explainability with validation, calibration, stability, and fairness diagnostics to support broader actuarial model governance.
Bakground:Salmonella spp. is a major cause of bacterial gastroenteritis worldwide borne from consuming contaminated food or water. The growing incidence of difficult-to-treat Salmonella infections has been heightened by increased AMR due to increased use of antibiotics posing acritical public health challenge. Methods:This was a cross-sectional study involving 205 children with AGE at Levy Mwanawasa University Teaching Hospital in Lusaka Zambia, between September 2020 and February 2021. Stool samples were collected and subjected to standard microbiological testing, serotyping, antimicrobial susceptibility testing and molecular confirmation for Salmonella spp. In addition, a questionnaire was administered to participants' guardians to determine the level of knowledge and practices towards Salmonella infections. Data analysis was performed using Microsoft Excel, GraphPad Prism and WHONET. Results:Twenty Salmonella isolates were recovered from the processed stool samples (n = 205), giving a 9.76% prevalence. Out of 20 Salmonella isolates identified, only four were susceptible to all tested antibiotics. Seven isolates (35%) were classified as multidrug resistant. The highest resistance was observed to trimethoprim-sulfamethoxazole (42.9%). Identified risk factors included use of untreated drinking water and suboptimal feeding practices. Conclusions:The presence of multidrug-resistant Salmonella among paediatric patients highlights the need for strengthened AMR surveillance, antimicrobial stewardship and targeted public health interventions.
BackgroundAntifungal resistance (AFR) is a growing global health threat, particularly in low- and middle-income countries such as Zambia, where antifungals are often accessed without prescription. Antifungal stewardship (AFS) programs aim to optimise antifungal use, yet limited data exist on pharmacy students' knowledge, attitudes, and practices (KAP) toward AFR and AFS. This study evaluated pharmacy students' knowledge, attitudes, and practices (KAP) toward AFR and AFS in Zambia.MethodsA descriptive cross-sectional survey was conducted among 288 randomly selected pharmacy students between August and September 2024 using a structured questionnaire. Descriptive statistics summarised demographics and KAP responses. Associations were analysed using Chi-square and Fisher's Exact Tests, while multivariable logistic regression identified predictors of good KAP (p < 0.05).ResultsAmong respondents (55.2% female; 76.7% aged 18–25 years), most students (94.1%) knew how to define AFR, and 90.3% recognized antifungal misuse as a factor driving resistance. Nevertheless, only 18.1% showed good knowledge, 67.7% held positive attitudes (especially among fourth-year students, p < 0.001), and merely 2.4% practiced appropriately. Multivariable analysis found that being a fourth-year student increased the likelihood of a good attitude (aOR = 5.10, 95% CI: 2.28–11.43). Married students were more likely to demonstrate good practices (aOR = 2.75, 95% CI: 1.13–6.68) towards AFR and antifungal stewardship (AFS) compared to unmarried ones. Additionally, age above 33 years was not significantly associated with better knowledge (aOR = 1.29, 95% CI: 0.32–5.18).ConclusionPharmacy students in Zambia demonstrated positive attitudes towards AFR and AFS, but significant gaps in knowledge and practice remain. Strengthening AFR and AFS-related content and experiential training within the pharmacy curriculum is urgently needed to improve preparedness for clinical practice.
