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Healthcare providers are under pressure due to increasing and more complex demands for services. Increased pressure on budgets and human resources adds to an ever-growing problem set. Competent leaders are in demand to ensure effective and well-performing healthcare organisations that deliver balanced results and high-quality services. Researchers have made significant efforts to identify and define determining competencies for healthcare leadership. Broad terms such as competence are, however, inherently at risk of becoming too generic to add analytical value. The purpose of this study is to suggest a holistic framework for understanding healthcare leadership competence, that can be crucial for operationalising important healthcare leadership competencies for researchers, decision-makers as well as practitioners.

In the present study, a critical interpretive synthesis (CIS) was conducted to analyse competency descriptions for healthcare leaders. The descriptions were retrieved from peer reviewed empirical studies published between 2010 and 2022 that aimed to identify healthcare services leadership competencies. Grounded theory was utilised to code the data and inductively develop new categories of healthcare leadership competencies. The categorisation was then analysed to suggest a holistic framework for healthcare leadership competence.

Forty-one papers were included in the review. Coding and analysing the competence descriptions resulted in 12 healthcare leadership competence categories: (1) character, (2) interpersonal relations, (3) leadership, (4) professionalism, (5) soft HRM, (6) management, (7) organisational knowledge, (8) technology, (9) knowledge of the healthcare environment, (10) change and innovation, (11) knowledge transformation and (12) boundary spanning. Based on this result, a holistic framework for understanding and analysing healthcare services leadership competencies was suggested. This framework suggests that the 12 categories of healthcare leadership competencies include a range of knowledge, skills and abilities that can be understood across the dimension personal – and technical, and organisational internal and – external competencies.

This literature review was conducted with the results of searching only two electronic databases. Because of this, there is a chance that there exist empirical studies that could have added to the development of the competence categories or could have contradicted some of the descriptions used in this analysis that were assessed as quite harmonised. A CIS also opens for a broader search, including the grey literature, books, policy documents and so on, but this study was limited to peer-reviewed empirical studies. This limitation could also have affected the result, as complex phenomenon such as competence might have been disclosed in greater details in, for example, books.

The holistic framework for healthcare leadership competences offers a common understanding of a “fuzzy” concept such as competence and can be used to identify specific competency needs in healthcare organisations, to develop strategic competency plans and educational programmes for healthcare leaders.

This study reveals a lack of consensus regarding the use and understanding of the concept of competence, and that key competencies addressed in the included papers are described vastly different in terms of what knowledge, skills and abilities they entail. This challenges the operationalisation of healthcare services leadership competencies. The proposed framework for healthcare services leadership competencies offers a common understanding of work-related competencies and a possibility to analyse key leadership competencies based on a holistic framework.

This study aims to identify and assess the key ethical challenges associated with integrating artificial intelligence (AI) in knowledge-sharing (KS) practices and their implications for decision-making (DM) processes within organisations.

The study employs a mixed-methods approach, beginning with a comprehensive literature review to extract background information on AI and KS and to identify potential ethical challenges. Subsequently, a confirmatory factor analysis (CFA) is conducted using data collected from individuals employed in business settings to validate the challenges identified in the literature and assess their impact on DM processes.

The findings reveal that challenges related to privacy and data protection, bias and fairness and transparency and explainability are particularly significant in DM. Moreover, challenges related to accountability and responsibility and the impact of AI on employment also show relatively high coefficients, highlighting their importance in the DM process. In contrast, challenges such as intellectual property and ownership, algorithmic manipulation and global governance and regulation are found to be less central to the DM process.

This research contributes to the ongoing discourse on the ethical challenges of AI in knowledge management (KM) and DM within organisations. By providing insights and recommendations for researchers, managers and policymakers, the study emphasises the need for a holistic and collaborative approach to harness the benefits of AI technologies whilst mitigating their associated risks.

First-line leaders across the emergency services are instrumental in leading the development of a workforce fit to face current and future challenges. As such in addition to utilising their specific craft, leaders need to be equipped to understand and apply evidence-based practices. With a focus on first-line leadership in policing, this paper will have both national and international resonance for those organisations attempting to embed an evidence-based culture.

The paper utilises a review of literature to develop a viewpoint identifying challenges and benefits of the adoption of evidence-based policing (EBP) by first-line leaders.

First-line leaders, whether police officers, police staff or volunteers, require opportunities to develop their own knowledge, understanding and skills of applying EBP in the workplace. Acknowledging challenges exist in the widespread adoption of EBP, such learning, at the appropriate educational level, will enable leaders to effectively champion the adoption of EBP, informing both their own decision-making and professional practices as well as those across their teams.

The first-line leader role is highly influential, as such, it is essential that these leaders develop their knowledge, understanding and application of EBP in the workplace in order to lead the expected cultural change.

This paper provides a current framework for the understanding of the context and potential impact of educationally levelled formal leadership learning required to champion the broad adoption of EBP across policing.

This purpose of this paper is to report on the development of an evidence-informed framework created to facilitate the formulation of generative artificial intelligence (GenAI) academic integrity policy responses for English medium instruction (EMI) higher education, responding to both the bespoke challenges for the sector and longstanding calls to define and disseminate quality implementation good practice.

A virtual nominal group technique engaged experts (n = 14) in idea generation, refinement and consensus building across asynchronous and synchronous stages. The resulting qualitative and quantitative data were analysed using thematic analysis and descriptive statistics, respectively.

The GenAI Academic Integrity Policy Development Blueprint for EMI Tertiary Education is not a definitive mandate but represents a roadmap of inquiry for reflective deliberation as institutions chart their own courses in this complex terrain.

If repeated with varying expert panellists, findings may vary to a certain extent; thus, further research with a wider range of stakeholders may be necessary for additional validation.

While grounded within the theoretical underpinnings of the field, the tool holds practical utility for stakeholders to develop bespoke policies and critically re-examine existing frameworks.

As texts produced by students using English as an additional language are at risk of being wrongly accused of GenAI-assisted plagiarism, owing to the limited efficacy of text classifiers such as Turnitin, the policy recommendations encapsulated in the blueprint aim to reduce potential bias and unfair treatment of students.

The novel blueprint represents a step towards bridging concerning gaps in policy responses worldwide and aims to spark discussion and further much-needed scholarly exploration to this end.

Higher education plays an essential role in achieving the United Nations sustainable development goals (SDGs). However, there are only scattered studies on monitoring how universities promote SDGs through their curriculum. The purpose of this study is to investigate the connection of existing common core courses in a university to SDG education. In particular, this study wanted to know how common core courses can be classified by machine-learning approach according to SDGs.

In this report, the authors used machine learning techniques to tag the 166 common core courses in a university with SDGs and then analyzed the results based on visualizations. The training data set comes from the OSDG public community data set which the community had verified. Meanwhile, key descriptions of common core courses had been used for the classification. The study used the multinomial logistic regression algorithm for the classification. Descriptive analysis at course-level, theme-level and curriculum-level had been included to illustrate the proposed approach’s functions.

The results indicate that the machine-learning classification approach can significantly accelerate the SDG classification of courses. However, currently, it cannot replace human classification due to the complexity of the problem and the lack of relevant training data.

The study can achieve a more accurate model training through adopting advanced machine learning algorithms (e.g. deep learning, multioutput multiclass machine learning algorithms); developing a more effective test data set by extracting more relevant information from syllabus and learning materials; expanding the training data set of SDGs that currently have insufficient records (e.g. SDG 12); and replacing the existing training data set from OSDG by authentic education-related documents (such as course syllabus) with SDG classifications. The performance of the algorithm should also be compared to other computer-based and human-based SDG classification approaches for cross-checking the results, with a systematic evaluation framework. Furthermore, the study can be analyzed by circulating results to students and understanding how they would interpret and use the results for choosing courses for studying. Furthermore, the study mainly focused on the classification of topics that are taught in courses but cannot measure the effectiveness of adopted pedagogies, assessment strategies and competency development strategies in courses. The study can also conduct analysis based on assessment tasks and rubrics of courses to see whether the assessment tasks can help students understand and take action on SDGs.

The proposed approach explores the possibility of using machine learning for SDG classifications in scale.

This paper aims to offer a commentary on Denne et al.’s (2024) article discussing the journey undertaken by the Sharland Foundation Developmental Disabilities Applied Behavioural Research and Impact Network to augment evidence surrounding the efficacy of the online Headsprout® Early Reading programme.

Drawing from over 15 years of professional experience as a qualified teacher in special education settings, the author reflects on the network’s approach and its alignment with their own expertise.

While acknowledging the commendable focus of the network on amassing additional evidence, this commentary underscores the importance of identifying barriers and fostering adaptability within the educational landscape. Central to the discussion is the imperative to prioritise students’ needs and tailor data collection methods to suit their unique contexts.

This commentary is aimed at professionals working in special education settings striving to improve their practice and student outcomes.

The attention of scholars and policy makers towards the topic of innovation has consistently increased, especially in recent years. This is justified by the fact that innovation undoubtedly plays, today, a crucial role in driving a country’s economic growth, improving productivity and, more generally, enhancing overall societal well-being.

When the discourse around innovation focuses on its economic dimension, the strong intertwinement with entrepreneurship emerges. In line with this, focusing on research on innovation in organisations and, especially, innovation in relation to the figure of the entrepreneur is considered, plenty of studies have been carried on, over time, in many disciplines, analysing the role of the entrepreneur in relation to innovation from various different angles. However, especially when management studies are considered, we can notice a poor consideration of the role played by the gender of the entrepreneur. In line with this consideration, by means of a systematic literature review, this chapter aims to fill this literature gap focusing on the intertwinement that can be envisaged, in management studies, among the issues of entrepreneurship and innovation in the case of women-owned firms.

Introduction: Small- and medium-sized enterprises (SMEs) play a vital role in the economic development of economies by generating job opportunities. Considering their significance, understanding the challenges and skills required in these enterprises becomes essential and timely.

Purpose: This study aims to discuss the limitations and skill gaps faced by SMEs in emerging economies, such as India, Indonesia, Brazil, China, Malaysia, Ghana, Hungary, Saudi Arabia, South Africa, Türkiye, UAE, Iran, Kazakhstan, Türkiye, Zambia, Romania, and Vietnam.

Methodology: The study adopts a systematic review and meta-synthesis approach, utilising a literature review to comprehensively analyse, synthesise, and map the existing literature by identifying overarching themes.

Findings: The study examines the challenges SMEs encounter in emerging economies, including resource scarcity, limited access to credit, inadequate infrastructure, low technology adoption, restricted global market access, and ineffective marketing strategies. There is a notable shortage of skilled labour and development initiatives within SMEs in India even though the country has a sizeable pool of qualified workers. There is a pressing need for additional technical and managerial skills to remain competitive in the market. The findings of this study will assist HR managers in addressing skill shortages among employees in SMEs operating within emerging economies

This study sets out to investigate the elements of the collaborative culture required for the successful implementation and sustainability of programs in schools. It draws on a case study of a student peer-led physical activity (PA) program implemented within the complex and dynamic environment of school communities in New Zealand. The article outlines four key components needed to effectively implement and impact long term sustainability of a program within the school context.

This qualitative case study examines the implementation of a new peer-led PA program introduced across eight New Zealand schools. Data were collected from semi-structured interviews with senior leaders, teachers and parents and analyzed through a complexity theory lens.

Effective and sustainable program implementation requires a strategic, collaborative approach through actively engaging with and resourcing four key interacting components: student choice, voice and agency; collective responsibility; shared understanding of purpose; and curriculum coherence.

This research offers a pragmatic approach to developing collaborative school communities that can effectively implement change by highlighting key areas of focus that policymaker, school leaders and program designers can plan for.

A systematic investigation of postgraduate leadership programs for health and/or human services offered by Australian higher education institutions was undertaken.

Quantitative analysis identified the core characteristics of the programs. A thematic analysis of the course learning outcomes was conducted and six major themes of disciplinary leadership and management knowledge; research and analytical skills; professional practice; communication and collaboration; creativity and innovation; and system knowledge are shared in this study.

The authors conclude that Australian universities have taken an evidence-based approach to leadership education.

More work might need to be undertaken to ensure leadership theories are incorporated into learning outcomes.