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Implications of student entrepreneurial traits on entrepreneurship education: a descriptiveInferential analysis
No full textAcross the globe, there is an increasing demand for entrepreneurship education due to the vital role entrepreneurs play in economic growth. However, to ensure the effective transmission of entrepreneurship education, it is essential to understand the entrepreneurial attributes of students. We profiled the attributes of students in entrepreneurship education at a university of technology in South Africa from a self-perception perspective. We collected data using a survey questionnaire, adopted a quantitative approach and used descriptive and inferential statistics to analyze data from 203 students. A census of the total student population was conducted, and all students willing to participate were included in the study. The results indicated that students reported high levels of entrepreneurial attributes. Students reported high levels of entrepreneurial traits, including confidently pursuing goals, perseverance through challenges, creative problem solving, adaptability in dynamic contexts, and effective networking. Although a few attributes showed a statistically significant distribution of perceptions among study levels and gender, the findings had important implications for supporting entrepreneurship education curricula and helping students enhance their entrepreneurial attributes. The findings underscore the value of self-perception in assessing entrepreneurial potential and suggest that a structured entrepreneurship education program can further enhance these attributes. These insights can guide educators in designing targeted programs that build on students' strengths while addressing development areas, contributing to a more inclusive and dynamic entrepreneurial ecosystem in South Africa. Future studies should explore entrepreneurial attributes across diverse institutional contexts to develop a comparative national perspective on entrepreneurship education in the countr
Substitution or complementarity? exploring trade credit use in African manufacturing MSEs
No full textAfrican manufacturing micro and small-sized enterprises (MSEs) depend heavily on supplier (trade) credit because formal bank finance is limited. Analyzing a dataset of around 10,846 firms across 45 African countries with an IV-probit approach, we find a large significant negative relationship between bank credit and trade credit, confirming that firms treat the two sources as substitutes. The substitution effect is strongest among credit-constrained MSEs and among mature firms (estimated coefficient ≈ –1.1), whereas very young start-ups show no significant effect. These patterns show how financing gaps and information asymmetries steer established MSEs toward supplier credit to fund working capital. Our study provides one of the most extensive multi-country pieces of evidence to date on trade-credit substitution in African manufacturing, our study points to several potential policy actions: expand credit registries to include inter-firm credit histories, scale supplier-finance tools such as factoring and reverse factoring, and provide prudential incentives, like lower capital requirements on receivables-backed loans. Such measures can help shift informal trade credit into the formal financial system and improve access to finance for constrained firms. These mechanisms should be designed to absorb a portion of the credit risk borne by lenders, thereby incentivizing increased lending to manufacturing MSEs
Integrating Magnetic Bead-Based SELEX with In Silico Binding Analyses for the Identification of High-Affinity DNA Aptamers Targeting TAGLN2
No full textThis study aimed to identify aptamers that bind with high affinity to transgelin-2 (TAGLN2), a potential diagnostic biomarker for a number of diseases, such as cancer and tuberculosis, that is associated with cellular stress. Aptamers targeting recombinant TAGLN2 were selected through magnetic bead-based systematic evolution of ligands by exponential enrichment (SELEX). DNA sequence analysis and Geneious software analysis identified 10 unique aptamer sequences that potentially bind to TAGLN2. A phylogenetic analysis of these sequences shows that these sequences clustered into two clades based on sequence similarity, with the sequences in one of the clades (consisting of 4 unique sequences) showing higher similarity to each other. The secondary structures of aptamer sequences (aptamers 1, 2, 7, and 9) from this highly conserved clade were predicted using the M-fold web server. The binding of two of these aptamer sequences (aptamer 7 and 9) to recombinantly expressed TAGLN2 was verified by microscale thermophoresis (MST). The dissociation constants for aptamers 7 and 9 were determined to be 18 ± 1 and 50 ± 2 nM, respectively. In silico analysis was used to perform molecular docking and molecular dynamics (MD) simulations between TAGLN2 and all 4 aptamers. These in silico analyses support the finding that aptamers 7 and 9 have a high affinity for TAGLN2 and that aptamer 7 has a higher binding affinity than aptamer 9. However, based on the in silico analysis, aptamer 1 may be a stronger binder than aptamer 7. This study demonstrates the advantages of integrating in silico analyses with SELEX to identify high-affinity aptamers. The TAGLN2 targeting aptamers identified in this study can potentially be used for the development of diagnostic tests for the detection of TAGLN2. This presents promising opportunities for early detection and intervention in diseases associated with increased levels of TAGLN2 expression
Network visualisation analysis of the transformative potential of generative AI tools in the education landscape
No full textThis research examines the transformative pathways of generative AI tools in the South African higher education landscape, directed by three research questions: (1) specific generative AI tools being utilised, and how are they applied across educational contexts? (2) What are the predominant AI techniques and software tools? (3) What education topics and issues are being addressed by these AI applications? Notwithstanding substantial potential, the acceptance of AI remains unpredictable, principally due to infrastructural insufficiencies, digital literacy gaps, and ethical concerns such as algorithmic bias. By means of the PRISMA methodology, this study conducts thematic and network visualisation analysis to map AI application pathways. Findings show that AI tools like ChatGPT and OpenAI GPT-3 are utililsed for automated grading → personalised learning → real-time feedback. Pathways show that these tools reform administrative responsibilities for educators (by reducing workload → refining teaching effectiveness) and support students through personalised learning experiences (adaptive tutoring → enhanced engagement → improved outcomes). Quantitative analysis reveals that AI tools like ChatGPT and OpenAI GPT-3 lead to a 20% reduction in educator workload, primarily through automated grading and content creation. Additionally, these tools contribute to a 15% improvement in student engagement, particularly through personalised learning pathways and real-time feedback. Key challenges are to develop robust ethical models to avert buttressing prevailing inequalities. This study aligns with the focus on knowledge management by highlighting how generative AI tools underscore the creation, distribution, and deployment of knowledge in educational settings, specifically through tailored learning and adaptive platforms. The study concludes that a custom-made and ethical amalgamation of AI is vital for leveraging its potential to develop educational outcome and equity in South African higher education
Using natural language processing in the LACE index scoring tool to predict unplanned trauma and surgical readmissions in South Africa
No full textBackground: Unplanned and potentially avoidable readmission within 30 days post discharge is a major financial burden. Aim: To use text-based electronic patient records to calculate the Charlson Comorbidity Index (CCI) score using a natural language processing technique to establish the feasibility and usefulness of the text-based electronic patient records in identifying patients at risk for unplanned readmission. Methods: A retrospective review of electronic patient records for general and trauma surgery in a hospital in South Africa (2012–2022) was conducted using the LACE score. Validated sentiment analysis analyzed free text components of electronic patient records to compute the CCI score and to establish the feasibility and usefulness of the LACE score in identifying patients at risk for unplanned readmission. Results: Trauma surgery patients had a mean LACE score of 5.91 (SD = 2.41), with 8.44% scoring 10 or higher and a specificity and sensitivity of 91.63% and 13.81%, respectively. The general surgery patients had a mean LACE score of 7.75 (SD = 3.04), with 10.63% scoring 10 or higher and a specificity of 71.47% and a sensitivity of 44.80%, respectively. Logistic regression analysis revealed that LACE scores significantly predicted unplanned readmissions in both trauma (β = 0.11, p < 0.001; OR = 1.112, 95% CI [1.082, 1.143]) and general surgery (β = 0.15, p < 0.001; OR = 1.162, 95% CI [1.130, 1.162]) patients. Conclusion: The LACE score demonstrated the predictive value for readmission in trauma and general surgery patients. The LACE score was relatively effective in identifying patients who were less likely to be readmitted but showed limitations in identifying patients at higher risk of readmission. However, the successful use of natural language processing for data extraction of comorbidities shows promise on addressing the challenges around text-based medical records
Multispectral remote sensing of groundwater dependent ecosystems in the Kruger National Park, South Africa
No full textGroundwater dependent vegetation (GDV) provides crucial ecosystem services for ecological and socio-economic development. The significance of GDV is amplified in semi-arid rangelands where they provide habitat, and forage for wildlife during the dry periods when surface water resources are scarce. Continuous assessment and conservation of groundwater-GDV in semi-arid rangelands, such as Kruger National Park (KNP) is crucial for maintaining ecosystem resilience and biodiversity amid the challenges posed predominantly by climate change. This study investigates the integration of remote sensing, machine learning, and climate modeling to delineate, map, and monitor the spatial distribution, diversity, and climate vulnerability of GDV in the Kruger National Park, South Africa. By utilizing cloud computing platforms like Google Earth Engine (GEE) in conjunction with open-source datasets and crowd-sourced field observations, this research sought to demonstrate the potential for conducting large-scale and fine-grained ecological assessments. To address the aim of this study the following specific objectives were drawn (i) to develop remote sensing-based techniques for delineating and mapping the spatial distribution of GDV in the Kruger National Park, (ii) to assess the potential of spatially explicit techniques to determine species diversity within groundwater dependent ecosystems (GDEs), (iii) to explore the spatio-temporal variations of vegetation diversity in GDEs and the driving forces for the observed changes and (iv)to determine climate change effects on the habitat suitability for GDV under the moderateclimate change scenario
Fine-scale mapping of irrigation suitability in South Africa using ensemble modelling
No full textFood insecurity, exacerbated by a growing population and environmental change, poses a significant challenge in Southern Africa. Enhancing agricultural productivity through efficient irrigation practices is crucial for achieving food and water security and sustainable development goals. This study applied an ensemble modelling approach to identify and assess irrigation suitability areas across South Africa, combining the predictive power of Random Forest, Extreme Gradient Boosting (XGBoost), and Gradient Boosting Machine (GBM) algorithms. These machine learning models were applied using cropland presence/pseudo-absence data and a suite of predictor variables. The ensemble model, leveraging a weighted averaging approach based on individual model performance, outperformed the individual models, achieving a TSS of 0.66 and an AUC of 0.90. Land use, population density, and elevation were identified as key factors determining irrigation suitability. The ensemble model also revealed substantial spatial variation in irrigation potential across South Africa, with the Northern Cape and Western Cape provinces exhibiting the largest suitable areas. The results provide critical information for targeted irrigation development, enabling efficient resource allocation, and maximising agricultural productivity. This data-driven approach offers a robust framework for sustainable agrarian planning in the face of increasing food demands and climate change, contributing to enhanced food security and economic development in South Africa
Global prioritised indicators for measuring WHO’s quality-of-care standards for small and/or sick newborns in health facilities: development, global consultation and expert consensus
No full textObjectives The aim of this study was to prioritise a set of indicators to measure World Health Organization (WHO) quality-of-care standards for small and/or sick newborns (SSNB) in health facilities. The hypothesis is that monitoring prioritised indicators can support accountability mechanisms, assess and drive progress, and compare performance in quality-of-care (QoC) at subnational levels. Design Prospective, iterative, deductive, stepwise process to prioritise a list of QoC indicators organised around the WHO Standards for improving the QoC for small and sick newborns in health facilities. A technical working group (TWG) used an iterative four-step deductive process: (1) articulation of conceptual framework and method for indicator development; (2) comprehensive review of existing global SSNB-relevant indicators; (3) development of indicator selection criteria; and (4) selection of indicators through consultations with a wide range of stakeholders at country, regional and global levels. Setting The indicators are prioritised for inpatient newborn care (typically called level 2 and 3 care) in high mortality/morbidity settings, where most preventable poor neonatal outcomes occur. Participants The TWG included 24 technical experts and leaders in SSNB QoC programming selected by WHO. Global perspectives were synthesised from an online survey of 172 respondents who represented different countries and levels of the health system, and a wide range of perspectives, including ministries of health, research institutions, technical and implementing partners, health workers and independent experts. Results The 30 prioritised SSNB QoC indicators include 27 with metadata and 3 requiring further development; together, they cover all eight standard domains of the WHO quality framework. Among the established indicators, 10 were adopted from existing indicators and 17 adapted. The list contains a balance of indicators measuring inputs (n=6), processes (n=12) and outcome/impact (n=9). Conclusions The prioritised SSNB QoC indicators can be used at health facility, subnational and national levels, depending on the maturity of a country’s health information system. Their use in implementation, research and evaluation across diverse contexts has the potential to help drive action to improve quality of SSNB care. WHO and others could use this list for further prioritisation of a core set
Academic freedom in dentistry is quietly eroding
No full textBackground: Academic freedom is under increasing pressure across higher education, yet its erosion in dentistry has remained largely unnoticed. Dentistry rarely features in discussions about academic freedom, despite facing a unique blend of institutional, cultural and political forces that narrow the space for independent thought and inquiry. Objective: This guest editorial aimed to highlight the quiet but profound erosion of academic freedom in dentistry. It examines how structural incentives, professional expectations and institutional dynamics are reshaping what can be thought, said and studied within dental schools. Key Arguments: Structural incentives and precarious work: The decline of tenure-track positions and the rise of contingent employment undermine the conditions for academic independence. Economic pressures, clinical productivity targets and tuition-driven business models reward conformity over curiosity. Metrics and research agendas: An excessive focus on performance metrics privileges what can be counted over what matters. Research funding structures reinforce this by prioritising clinical and basic sciences while sidelining public health and interdisciplinary perspectives. The ‘triple threat’ trap: The traditional expectation of excellence in teaching, research and service, now compounded by clinical revenue generation, has become a structural contradiction. It leaves little space for reflection, critical engagement or dissent. Internalized pressures: Political interference compounds the problem, but the deeper erosion comes from within. Institutional risk aversion, reputational control and self-censorship operate silently, narrowing the scope of academic discourse before external pressures even arrive. Implications for Dental Education and Scholarship: When academic freedom is curtailed, scholarship contracts. Public engagement becomes riskier, critical enquiry fades, and the profession's ability to interrogate itself diminishes. This weakens dentistry's intellectual and societal role. Conclusion: Academic freedom is not a privilege or a romantic ideal; it is a shared responsibility. It must be practiced, protected and supported through valuing critical engagement, creating institutional space for intellectual risk and recognizing dissent as integral to scholarship. Defending this freedom is essential if dentistry is to remain a space for curiosity, reflection and meaningful contributions to public health
Beyond revictimisation of women: Engaging the male perpetrators’ voice in rethinking intimate partner violence in South Africa
No full textWorld Health Organization (WHO) defines intimate partner violence as violence by a person against a former or current partner, and includes verbal abuse, emotional abuse, financial abuse, controlling behaviours and sexual abuse (WHO, 2021). IPV victimisation is much more common among women in South Africa than among men. In addition to being a human rights violation, IPV is a global public health issue. This study examined the causes of IPV from the incarcerated male perpetrators’ viewpoints, explored the correctional centres’ rehabilitation programs from the perspectives of the male perpetrators and provided possible mitigation strategies. Intersectional, standpoint, and social construction theories underpinned the theoretical framing of the study, suggesting that marginalised people’s realities are shaped within their social locations, and that incarcerated male IPV perpetrators’ lived experiences should be examined through an awareness of their intersectional identities. To obtain a deeper understanding of the participants' lived experiences, the study employed a feminist qualitative approach. The data was analysed using qualitative thematic analysis. I conducted in-depth interviews with 20 incarcerated men who had perpetrated IPV in heterosexual relationships. The findings indicate that alcohol abuse is a conspicuous marker that leads to abuse in intimate relationships. Additionally, the study notes that jealousy, anger, and infidelity are driving forces of IPV, which is also fuelled by the lack of a father figure as a role model and experiencing childhood abuse. Perpetrators acknowledge that violence against women is wrong and want to stop it. However, they reject the notion of gender equality, as it threatens their standing as males. They pointed to a need for anger management programmes to be accessible in communities, as it seems most men need them