26564 research outputs found
Sort by
Leadership style's role in fostering supply chain agility amid geopolitical shocks
No full textGeopolitical shocks can disrupt businesses on multiple fronts, such as sudden loss of buyers or suppliers, supply disruption and demand fluctuations, wielding the power to profoundly undermine a firm's ability to meet customer demands. The imperative to grasp the repercussions of geopolitical tumult and devise effective countermeasures has therefore drawn the attention of scholars and business leaders alike. Nevertheless, the empirical research in this area remains limited. This study addresses this gap by using 247 Australian manufacturing and distribution firms with a global footprint. We find that geopolitical shocks are inextricably linked to a decline in firms' supply chain agility. Our analysis further reveals that while crisis leadership and transformational leadership, when practiced alone, have limited effectiveness in mitigating the adverse effects of geopolitical shocks on supply chain agility, their combined application significantly reduces the negative impact. The synergistic approach highlights the significance of crisis leadership for immediate needs and swift action, along with transformational leadership for long-term strategies, vision, and continuous improvement, ensuring lasting business success. These insights are valuable for businesses aiming to enhance their resilience in the face of geopolitical uncertainties and stay competitive.</p
Strong Beginnings Matter: Building Collaborative Partnerships Between Schools and Indigenous Communities to Support a Smooth Transition to Kindergarten
No full textTransitioning to primary or elementary school is a transformative time for children, families, and school communities. However, today, many children, particularly those from historically marginalised backgrounds, are at a greater risk of a compromised start to school (Ansari & Crosnoe, 2018; Dockett et al., 2014; Piotrkowski et al., 2000). With a strong early start to school often associated with later academic success, it is critically important for schools to understand the complexities experienced by families and children during the transitional period (Atchison & Pompelia, 2018; Bornfreund et al., 2020). Research suggests that enhanced family-school engagement in the transition to school process can promote a positive start to school for all children (Dockett & Perry, 2021; Margetts Kienig, 2013). The following qualitative research project explores the diverse factors that influence the engagement of families from a Native American community in the northwest of the United States in the transition to school. Guided by an Indigenous paradigmatic approach rooted in an Indigenous ontology and relational epistemology, the study engaged familial and educator voices and perspectives to identify strategies and practices critical to building strong family-school partnerships and promoting an equitable start to school. The practice of reflexivity was a core tenet of this qualitative inquiry and a guide to conducting ethical research in an Indigenous context. The research design drew on Kirkness and Barnhardt’s (1991) four Indigenous axiological principles (respect, relevance, reciprocity, and responsibility) to ensure the study was respectful and culturally safe for Indigenous participants. Using a cultural interface of relationality and Bronfenbrenner's Bioecological Framework (1979, 2005), the transition to school was framed as a cultural process. Data derived from conversations with families and educators were analysed using Braun and Clark’s (2022) reflexive thematic approach. This analysis revealed three key themes: (1) Strong beginnings: Create culturally inclusive learning environments; (2) Strong beginnings: Build culturally safe family partnerships; and (3) Strong beginnings: Respond to a community’s culturally specific needs. These themes highlighted the importance of nurturing culturally safe partnerships with families in tandem with specifically designed policies and practices that prioritise a culturally responsive approach to the transition to school. Policies and practices promoting school-family partnerships are critical to ensuring a positive, culturally equitable start to school for all children and have the potential to change the transition to school experiences for Indigenous families and children. Considering the growing cultural diversity within school settings and student populations, the findings of this research could provide valuable insights into the broader cultural context of the transition to school. Findings may have applications beyond the scope of the school central to this study’s collaboration with families and across cultural boundaries by informing schools’ approaches to local family engagement policies and practices and suggesting ways to strengthen partnerships with all families during the transition-to-school process. The implications of this study suggest a need to re-envision family engagement in the transition-to-school process as the cornerstone of inclusivity for all families and children from diverse backgrounds. Reimagining family engagement as a collaborative endeavour ushers in an age where building better partnerships with families and embracing their unique perspectives is crucial and widely accepted as the ‘new normal’. Approaching transition-to-school practices with an otherwise viewpoint that is responsive to a child and their families cultural heritage is essential to supporting a positive start to school. In navigating this doctoral thesis journey, I have been inspired to reconsider my role as a teacher and the profound importance of enhancing family engagement during the transition to school. The experiences of children and families during the transition to school process are critical to establishing an equitable foundation for all children as they begin their schooling journey. Strong beginnings matter.</p
Simulation-based Ultrasound in Sonographer Education: A Retrospective Evaluation Case Study
No full textSonographers are practice-based medical imaging professionals who perform diagnostic ultrasound examinations. Australia has a sonographer shortage that risks patients’ timely access to ultrasound services. Limited access to practical training in clinical departments is cited as the predominant cause of the shortage. Educational change, such as practical training using simulation-based ultrasound, may provide a solution. Simulation-based ultrasound is a teaching method used to educate medical students to ultrasound proficiency, however it is not broadly integrated within Australian sonography curricula. Research into the value and impact of ultrasound simulation on prequalification sonographer students’ learning is limited. This research aims to determine the value and impact of simulation-based ultrasound training on prequalification sonographer students’ anatomical knowledge, ultrasound (skill) performance, and professional behaviour. Two literature reviews were undertaken. The first review of grey literature was undertaken to identify issues impacting the sonographer workforce shortage and any educational response. The second review of published literature determined the value and impact of simulation-based ultrasound on students’ learning. The first review revealed a decade of growth in sonographer student numbers but increasing demand that ensured the persistence of workforce shortage. Educational responses included two new models of sonographer education with integrated simulation-based ultrasound training. The second review revealed the positive value and impact of simulation-based ultrasound on students’ learning of anatomical knowledge and ultrasound performance. Research rarely included sonographer students as participants, ultrasound simulation outcomes for professional behaviour, or evaluation of the transfer of students’ learning to the clinical workplace. Research into these evidentiary gaps is a prerequisite to the widespread adoption of simulation-based ultrasound into sonographer education. This thesis undertakes an evaluative case study methodology. The case was the simulation-based ultrasound education integrated into the sonographer students’ curriculum at Central Queensland University (CQUniversity), Queensland, Australia. The case study sought to evaluate the outcomes of simulation-based ultrasound training within a five-year timeframe. Methods of data collection and analysis were threefold, comprising qualitative analysis of student surveys, and both quantitative, and qualitative, analyses of university archival data that reported clinical supervisor-assessors’ numeric ratings, and written comments. The student participants were limited to prequalification students enrolled at CQUniversity. Research findings demonstrated the positive impact on and efficacious transfer of post-simulation sonographer students’ ultrasound performance and professional behaviour to the clinical workplace. Secondly, they demonstrated sonographer and radiographer students’ positive perception of the value of ultrasound scanning (in a simulated setting) as an anatomical teaching method. Other findings included that the clinical transfer of learnt skills and behaviours was non-uniform; deliberate practice appeared to improve transferability; and factors were identified that influenced sonographer assessors’ judgements of post-simulation competence. Such evidence is critical to the validation, adoption, assessment, and integration of simulation-based ultrasound into sonographer education curricula. Simulation-based ultrasound training has the potential to enhance clinical outcomes, professional identity development, workplace relationships, clinical workflow, and patient well-being during the early stages of ultrasound education, while reducing staff turnover, and litigation risks. Implementation of simulation-based ultrasound could potentially increase clinical training capacity and thereby improve sonographer workforce supply. In turn, this would reduce the training burden on clinical staff.</p
Responsible artificial intelligence (AI) in healthcare: a paradigm shift in leadership and strategic management
No full textPurpose – This paper aims to explore the paradigm shift in leadership and strategic management driven by the integration of responsible artificial intelligence (AI) in healthcare. It explores the evolving role of leadership in adapting to AI technologies while ensuring ethical governance, transparency and accountability in healthcare decision-making. Design/methodology/approach – This study conducts a comprehensive review of current literature, case studies and industry reports to evaluate the implications of responsible AI adoption in healthcare leadership. It focuses on key areas such as AI-driven decision-making, resource optimisation, crisis management and patient care, while also addressing challenges in integrating AI technologies effectively. Findings – The integration of AI in healthcare is transforming leadership from traditional, experience-based decision-making to data-driven, AI-enhanced strategies. Responsible leadership emphasises addressing ethical concerns such as bias, transparency and accountability. AI technologies improve resource allocation, crisis management and patient care, but challenges such as workforce resistance and the need for upskilling healthcare professionals remain. Practical implications – Healthcare leaders must adopt a responsible leadership framework that balances AI’s potential with ethical and human-centred care principles. Recommendations include developing AI literacy programmes for healthcare professionals, ensuring inclusivity in AI algorithms and establishing governance policies that promote transparency and accountability in AI applications. Originality/value – This paper provides a critical, forward-looking perspective on how responsible AI can drive a paradigm shift in healthcare leadership. It offers novel insights into the integration of AI within healthcare organisations, emphasising the need for leadership that prioritises ethical AI usage and promotes patient well-being in a rapidly evolving digital landscape.</p
“We know the past: what is the future for indigenising English curriculum?”
No full textPurpose – The importance of English teachers embedding Indigenous perspectives is recognised within the Australian national curriculum through its cross-curriculum priority and professional teacher standards. The purpose of this study is to review the selection and availability of Indigenous texts in senior high school subject English in the Australian state of Queensland, discussing teacher considerations and challenges. Design/methodology/approach– The researchers used content analysis to examine the prescribed text lists for senior subject English in Australia. Specifically, the authors examined the 2025 and 2026 lists in Queensland Curriculum and Assessment Authority (QCAA) English General focusing on external assessment texts; novels and prose texts; plays and drama texts. Findings– This study found that while many texts are written by Australian authors, very few are authored by First Nations writers. There is a heavy reliance on American and British texts. Texts date from the pre1600s. Only two new texts have been written since 2020. A range of genres is offered. There are some common themes across Indigenous and non-Indigenous texts, but there are also unique points of difference. Originality/value– This enquiry offers recommendations for teachers of subject English and meeting the Australian Professional Teacher Standards (APSTs). While this enquiry focuses on the Australian context, the findings could be useful to educators working with minority Indigenous groups globally.</p
A theory of coexistence: healthcare educators assuming simulated patient roles—a grounded theory study
No full textEducators don hospital gowns, steady their breathing, and assume the roles of simulated patients. Their voices quiver with carefully cultivated anxiety as students enter simulation suites. As the scenario unfolds, educators wonder: will the students learn? Will their objectives be met? Simultaneously, they juggle the tension between authenticity and pedagogy. This study employed constructivist grounded theory to construct a theoretical framework from 19 semi-structured interviews with simulation educators, exploring their experiences and the consequences of assuming simulated patient roles—a perspective rarely examined in healthcare education. While student learning is often prioritised, little attention has been given to the emotional, psychological, and professional tolls on educators. Addressing this gap, the thesis introduces a theory of coexistence, conceptualising the tension educators experience as they navigate dual roles: altruistic facilitators of student learning and self-preserving individuals managing emotional and professional needs. This framework positions competing motivations—altruism and egoism—within the broader context of educator identity, highlighting inherent conflicts and their coexistence.Findings reveal educators were torn between dedication to student learning and concerns for recognition, well-being, and personal fulfilment. They grappled with professional responsibility, balancing realism and authenticity while questioning whether their work was valued as education rather than performance. Some found the experience rewarding; others faced internal conflict and emotional strain. Institutional gaps compounded these challenges. Simulated patient roles were integrated without sufficient policies, standards, or safeguards. Participants reported a lack of formal recognition, structured debriefing, and professional support, raising ethical concerns and threatening workforce sustainability. Emotional engagement often led to exhaustion and difficulties detaching from roles, with educators left to manage consequences alone. These findings underscore the urgent need for policies, education, and institutional frameworks to protect educators’ well-being and strengthen simulation-based education. By centring educators in these discussions, this research advances theoretical understanding of educator identity and role immersion, while emphasising the need for regulatory structures that formally recognise and support their contributions. Behind each transformative simulation is an educator carrying the silent weight of performance, care, and personal sacrifice—efforts that deserve recognition and protection.</p
Track buckling predictions using long track dynamics models and machine learning
No full textRailway track buckling is a critical structural failure phenomenon where continuous welded rails (CWR) deform laterally due to thermal stresses, dynamic loads, or a combination of both. This instability can compromise the track structure, leading to severe safety risks and operational disruptions. Understanding the mechanisms behind track buckling is essential for developing effective strategies to predict, prevent, and mitigate such failures. Despite extensive studies in existing literature, opportunities remain for further investigation into comprehensive simulations of long track dynamics. While virtually infinite track dynamics simulations exist and provide valuable insights, they do not fully represent extended continuous track infrastructure systems, as they are based on assumptions that may not capture the full range of complexities present in such systems. Furthermore, the interplay between dynamic forces and buckling behaviour over realistic track lengths remains an area where simulation studies are relatively scarce, indicating opportunities for further investigation. This thesis advances the field by developing computational models that integrate advanced simulation techniques with predictive analysis. Its aim is to establish a cohesive framework for modelling CWR buckling risks by deepening the understanding of track dynamics under combined static, thermal, and dynamic forces. One key objective of this thesis is to develop a physics-based, numerical dynamic railway track model capable of simulating both buckling phenomena and long tracks. Here, “long” refers to track lengths sufficient to accommodate realistic train configurations, typically in the range of 2 to 3 kilometres. The model is designed to leverage the finite element method for rail representation, mass block models for the sleepers and the underlying supports, and parallel computing techniques using the message passing interface framework to efficiently simulate kilometre-scale track dynamics under varied operational scenarios. A further objective is to ensure that the model captures the nonlinear responses of the track system to combined thermal stresses and dynamic lateral loads, enabling representation of track dynamics under diverse loading and environmental conditions. Another central objective is to incorporate uncertainties in critical parameters such as rail temperature, lateral resistance, misalignment, and ballast properties into a probabilistic framework based on Monte Carlo simulations to assess track stability. To manage the computational demands of kilometre-scale simulations, a further objective is to develop a machine learning-based surrogate model that accelerates evaluation while maintaining predictive fidelity. This stochastic approach to buckling risk evaluation enhances the reliability of buckling risk assessments by accounting for variability in dominant buckling drivers and paves the way for real-time predictive capabilities in railway infrastructure management. The thesis synthesises numerical methods with contemporary data-driven techniques, creating a hybrid modelling framework that bridges the gap between physics-based simulations and machine learning. This integrated approach enhances both the predictive capability and operational applicability of the models. This thesis demonstrates the feasibility of kilometre-scale dynamic buckling simulations through parallelised physics-based modelling and establishes a probabilistic framework that captures variability in key buckling parameters. It further shows that the machine learning-based surrogate model achieves strong predictive consistency with the physics-based simulations, exceeding 90% agreement. Together, these advances contribute a unified, scalable, and configurable framework that enhances both the predictive capability and operational applicability of buckling risk assessments. The framework developed in this thesis provides insights for asset maintenance and risk management, enabling safer and more resilient railway operations. By integrating advanced simulation techniques, probabilistic analysis, and machine learning, it delivers new strategies for mitigating track buckling and advances current practice in railway infrastructure management.</p
Comparable achievement of workplace performance of student and graduate nurses: A quantitative cohort evaluation
No full textAim: This study explored the achievement of workplace capabilities synonymous with nursing practice. It investigated progressive development of workplace performance, against requisite standards of practice, of corresponding cohorts of student nurses during their final year placement and registered nurses during their graduate year, in south-eastern Queensland, Australia. Background: A capable nursing workforce is vital for the provision of safe, quality health care. Internationally, the literature discusses the breadth of capacities required for the practice of nursing. A perennial challenge is comprehensive pre-registration education that ensures new graduate nurses meet standards for practice to provide comprehensive care. Methods: A quantitative evaluation design was used to assess the progressive development of workplace performance of both student nurses across their final year placement (n = 214) and graduate nurses in their first nine months (n = 197). Capabilities of both cohorts were assessed using the Australian Nursing Standards Assessment Tool (ANSAT), based on Australian registered nurses’ standards for practice that has demonstrated utility and validity for both students and graduates. Results: A total of 642 student assessments and 409 graduate nurse assessments were used for analysis. Findings revealed congruence in weaknesses in workplace performance for both students and graduates. Students and graduates consistently rated lowest in capabilities pertaining to high cognitive tasks that involved analysing data and modifying plans. Of interest, completing comprehensive, systematic assessments was stronger in students than in graduates. Conclusion: This is the first comparable quantitative study exploring the progressive development of students and graduate nurses’ workplace performance aligned to nursing practice standards. Questions of limitations throughout educational learning experiences during industry placement are raised in these findings. Furthermore, it is proposed that educational approaches be organised to address areas identified as weakest on graduation as these are most evident in initial employment. This research explicates a largely undefined area of nursing practice that can inform undergraduate learning priorities.</p
11 things you and your partner must talk about
No full textThey are common sources of conflict for couples and can lead to feelings of frustration and disconnection if not discussed.</p
Advancements and challenges in numerical analysis of hydrogen energy storage methods: Techniques, applications, and future direction
No full textHydrogen has a high energy density and zero emissions but is also highly flammable with low volumetric energy content. Hydrogen storage plays a crucial role in advancing clean energy technologies. Numerous strategies are being explored to address the challenges associated with its storage and controlled release. This article reviews the developments and challenges in the theoretical modeling of hydrogen storage mechanisms. The paper discusses methods for numerical simulations, including Finite element method (FEM), Computational Fluid Dynamics (CFD), and Molecular Dynamic (MD) models, as well, all of which are main contributors to elucidating basic mechanisms of operation and improving storage placements. Finally, the paper addresses some current issues and constraints suffered by numerical simulations including computational cost, accuracy, and scalability. Discussed future directions and opportunities in numerical analysis for hydrogen storage research are about the integration of multiscale modeling and machine learning with experimental validation to expedite innovation and support the transition towards sustainable hydrogen-based energy systems.</p