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.
Exposure to toxic metals and metalloids is an important public health concern because they can damage the nervous system and impair cognitive and motor function. Oxidative stress, disturbed glutathione balance, mitochondrial dysfunction and neuroinflammation are common features of this damage. Nuclear factor erythroid 2-related factor 2 (NRF2) regulates proteins that help cells maintain redox balance and withstand toxic stress. Increased NRF2 expression after metal exposure is therefore often interpreted as evidence of protection. However, expression alone does not show that the pathway is working effectively. Functional activation requires NRF2 nuclear translocation, binding to antioxidant-response elements, induction of target genes and improvement in redox or mitochondrial function. This review critically examines human, animal and cellular evidence concerning NRF2 responses to cadmium, lead, mercury, arsenic and manganese. It considers how dose, duration, brain region and cell type shape these responses and evaluates compounds intended to modify NRF2 signalling. It assesses whether NRF2 dependence was demonstrated using genetic deletion, knockdown, pharmacological inhibition or rescue rather than inferred from expression changes. Current evidence supports NRF2 as an important but context-dependent defence mechanism. However, causal evidence remains uneven, human data are limited and many proposed activators have effects beyond NRF2. Future studies should use environmentally relevant exposures and directly measure pathway activity, mitochondrial function and neurological outcomes. This is necessary to determine whether NRF2 modulation can provide safe and meaningful protection against environmental neurotoxicity.
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.
Digital inclusion remains a challenge in rural South African secondary schools, where inadequate infrastructure, limited connectivity, financial constraints and uneven teacher digital capabilities restrict meaningful technology integration. This study explored stakeholders’ perceptions of the contribution of community-supported digital inclusion initiatives to digital integration and perceived learner engagement in five rural secondary schools in Nkangala District, Mpumalanga Province. Guided by an interpretivist paradigm and Social Capital Theory and Human Capital Theory, the study employed a qualitative exploratory design. Data were collected through semi-structured interviews with 10 school administrators and five community leaders, five focus group discussions involving 25 teachers, and document analysis. Thematic analysis using a hybrid inductive-deductive approach generated five themes: 4IR relevance and readiness; institutional barriers and sustainability; community partnerships and strategic planning; pedagogical adaptation; and community-supported capacity-building and teacher agency. Findings indicate that stakeholders perceived community initiatives as facilitating access to devices, connectivity, training, technical support and partnerships. Teachers also reported adapting their practices through peer learning, experimentation and contextually appropriate technologies. However, sustainability was constrained by infrastructure deficiencies, limited maintenance resources, bureaucratic delays, inadequate institutional planning and dependence on external partners. The study conceptualises rural digital inclusion as an adaptive, relational and institutionally mediated process shaped by community networks, teacher capabilities and institutional conditions. Sustainable inclusion requires continuous teacher development, participatory school-level digital planning, strategic partnerships, reliable infrastructure and stronger institutional support. Community initiatives should complement, rather than replace, the state’s responsibility to address structural digital inequalities.
