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The broad aim of this paper is to use Noddings' theory of ethical care to analyse mentors' caring experiences. More specifically, it aims to analyse how physical education (PE) mentors provide care, how they are cared for and how this impacts their role within the context of secondary PE initial teacher training (ITT).

Semi-structured interviews were used to generate data from 17 secondary PE mentors within the same university ITT partnership in the north-west of England. Questions focused on the mentors' experiences of care and the impact this had on their wellbeing and professional practice. A process of thematic analysis was used to identify, analyse and report patterns in the data.

The participants reflected established definitions of mentoring by prioritising the aim of developing the associate teachers' (ATs) teaching rather than explicitly providing support for their wellbeing. This aim could be challenging for mentors who face personal and professional difficulties while supporting the training of an AT. Mentors frequently referred to the support of their departmental colleagues in overcoming these difficulties and the importance of developing interdependent caring relationships. Receiving care did not impede mentors from providing support for others; it heightened awareness and increased their desire to develop caring habits.

Teacher wellbeing has drawn greater attention in recent years and is increasingly prioritised in public policy. These findings highlight the value of mentor wellbeing and how caring professional relationships can mitigate the pressures associated with performativity and managing a demanding workload.

The internal (farmer-controlled) and external (non-farmer-controlled) factors affect crop yield. However, not a single study has identified and analyzed yield predictors in India using effective predictive models. Thus, this study aims to investigate how internal and external predictors impact pearl millet yield and Stover yield.

Descriptive analytics and artificial neural network are used to investigate the impact of predictors on pearl millet yield and Stover yield. From descriptive analytics, 473 valid responses were collected from semi-arid zone, and the predictors were categorized into internal and external factors. Multi-layer perceptron-neural network (MLP-NN) model was used in Statistical Package for the Social Sciences version 25 to model them.

The MLP-NN model reveals that rainfall has the highest normalized importance, followed by irrigation frequency, crop rotation frequency, fertilizers type and temperature. The model has an acceptable goodness of fit because the training and testing methods have average root mean square errors of 0.25 and 0.28, respectively. Also, the model has R² values of 0.863 and 0.704, respectively, for both pearl millet and Stover yield.

To the best of the authors’ knowledge, the current study is first of its kind related to impact of predictors of both internal and external factors on pearl millet yield and Stover yield.

The literature reveals that most studies have estimated crop yield using limited parameters and forecasting approaches. However, this research will examine the impact of various predictors such as internal and external of both yields. The outcomes of the study will help policymakers in developing strategies for stakeholders. The current work will improve pearl millet yield literature.

The study deploys Anthias' intersectional framework of social spaces and her concept of translocational positionality to explore the barriers to entrepreneurship for refugee entrepreneurs in the United Kingdom (UK). In particular, the study aims to assess how migrant identities require a specific form of business support.

A total of 32 semi-structured interviews with 14 refugee entrepreneurs and 18 business support agents were conducted between April and October 2022 and, together with field notes, were combined for thematic analysis in NVivo 12.

Organisational, representational, intersubjective and experiential barriers combined to create practical and psychological deterrents to entrepreneurship for refugees. However, an explicitly humanistic and de-centred approach to business support was (partially) able to counter such barriers.

Policymakers and business support agencies should consider intersectional characteristics and the importance of a compassionate and individual approach when designing business support programmes for refugee entrepreneurs.

Two intersectional concepts of social spaces and translocational positionality are brought into conversation with each other, creating a novel approach to framing the barriers to entrepreneurship for refugees.

This research addresses a need in early childhood education for evidence-based teaching strategies that build emotional self-regulation skills in young children. The intervention assessed in this study focused on increasing the emotion vocabulary of preschool-aged students.

This mixed-methods, quasi-experimental study evaluated the impact a dialogic reading approach combined with direct instruction of emotion words during a shared book-reading activity had on students' emotion vocabulary knowledge. The study was conducted in a licensed daycare center in a suburb of Chicago, Illinois, with ten four- and five-year-old students. Pre- and post-session surveys assessed the intervention's impact on the students' receptive and expressive vocabulary knowledge, and observation notes captured the students' responses to the intervention activities.

The results showed significant increases with small to medium effect sizes between the students’ pre- and post-session survey scores for both receptive and expressive emotion vocabulary knowledge, a strong positive correlation between the level of student engagement during the intervention and their emotion vocabulary assessment scores, and the impact other variables had on the intervention’s effectiveness.

This research provides information on a culturally adaptable and quickly learned teaching strategy that could be used to build emotional self-regulation skills in the early childhood classroom.

This research uniquely applies this intervention as a universal strategy with preschool-aged children.

College students who are interested in experiencing and learning about other cultures could be potential agents to ongoing social and policy initiatives in promoting societal changes. As universities intensify their efforts toward embracing cultural diversity, it is imperative to gauge how these diversity initiatives resonate with students' developmental stage and pursuits in diverse campus climates. However, what kinds of educational experiences/contexts students choose for enhancing cultural competence, and how seeking diversity experiences might benefit college students in emotional wellbeing and cognitive skills, are under-investigated.

This study explores the relationships among college students' diversity-seeking behaviors, cultural competence, perspective-taking, and flourishing. A total of 359 college students from a STEM-focused university participated in this study. Students were recruited from classes over four semesters, from 2021 to 2023.

Students exhibited moderate to high levels of interest in seeking diversity in their learning experiences. Results from the structural equation modeling showed that higher levels of diversity-seeking in learning were associated with higher levels of perceived cultural competence, as well as higher levels of perspective-taking and flourishing.

This research delves into experiential and extracurricular dimensions of learning diversity, bridging a significant gap in academic literature. This study also elucidates the links between aspects of diversity engagement, cultural competence, and positive outcomes for college students, which underscores the significance of diversity-focused educational opportunities in higher education. Such opportunities are instrumental in enhancing cultural proficiency and further implications on cognitive growth and emotional well-being.

Deep learning (DL) is a new and relatively unexplored field that finds immense applications in many industries, especially ones that must make detailed observations, inferences and predictions based on extensive and scattered datasets. The purpose of this paper is to answer the following questions: (1) To what extent has DL penetrated the research being done in finance? (2) What areas of financial research have applications of DL, and what quality of work has been done in the niches? (3) What areas still need to be explored and have scope for future research?

This paper employs bibliometric analysis, a potent yet simple methodology with numerous applications in literature reviews. This paper focuses on citation analysis, author impacts, relevant and vital journals, co-citation analysis, bibliometric coupling and co-occurrence analysis. The authors collected 693 articles published in 2000–2022 from journals indexed in the Scopus database. Multiple software (VOSviewer, RStudio (biblioshiny) and Excel) were employed to analyze the data.

The findings reveal significant and renowned authors' impact in the field. The analysis indicated that the application of DL in finance has been on an upward track since 2017. The authors find four broad research areas (neural networks and stock market simulations; portfolio optimization and risk management; time series analysis and forecasting; high-frequency trading) with different degrees of intertwining and emerging research topics with the application of DL in finance. This article contributes to the literature by providing a systematic overview of the DL developments, trajectories, objectives and potential future research topics in finance.

The findings of this paper act as a guide for literature review for anyone interested in doing research in the intersection of finance and DL. The article also explores multiple areas of research that have yet to be studied to a great extent and have abundant scope.

Very few studies have explored the applications of machine learning (ML), namely, DL in finance, which is a much more specialized subset of ML. The authors look at the problem from the aspect of different techniques in DL that have been used in finance. This is the first qualitative (content analysis) and quantitative (bibliometric analysis) assessment of current research on DL in finance.

The advent of technology has propelled audit firms to incorporate AI-based audit services, bringing the relationship between audit clients and firms into sharper focus. Nonetheless, the understanding of how AI-based audit services affect this relationship remains sparse. This study strives to probe how an audit client's satisfaction with AI-based audit services influences their trust in audit firms. Identifying the variables affecting this trust, the research aspires to gain a deeper comprehension of the implications of AI-based audit services on the auditor-client relationship, ultimately aiming to boost client satisfaction and cultivate trust.

A conceptual framework has been devised, grounded in the client-company relationship model, to delineate the relationship between perceived quality, perceived value, attitude and satisfaction with AI-based audit services and their subsequent impact on trust in audit firms. The research entailed an empirical investigation employing Facebook ads, gathering 288 valid responses for evaluation. The structural equation method, utilized in conjunction with SPSS and Amos statistical applications, verified the reliability and overarching structure of the scales employed to measure these elements. A hybrid multi-analytical technique of structural equation modeling and artificial neural networks (SEM-ANN) was deployed to empirically validate the collated data.

The research unveiled a significant and positive relationship between perceived value and client satisfaction, trust and attitude towards AI-based audit services, along with the link between perceived quality and client satisfaction. The findings suggest that a favorable attitude and perceived quality of AI-based audit services could enhance satisfaction, subsequently augmenting perceived value and client trust. By focusing on the delivery of superior-quality services that fulfill clients' value expectations, firms may amplify client satisfaction and trust.

Further inquiries are required to appraise the influence of advanced technology adoption within audit firms on client trust-building mechanisms. Moreover, an understanding of why the impact of perceived quality on perceived value proves ineffectual in the context of audit client trust-building warrants further exploration. In interpreting the findings of this study, one should consider the inherent limitations of the empirical analysis, inclusive of the utilization of Facebook ads as a data-gathering tool.

The research yielded insightful theoretical and practical implications that can bolster audit clients' trust in audit firms amid technological advancements within the audit landscape. The results imply that audit firms should contemplate implementing trust-building mechanisms by creating value and influencing clients' stance towards AI-based audit services to establish trust, particularly when vying with competing firms. As technological evolutions impinge on trustworthiness, audit firms must prioritize clients' perceived value and satisfaction.

To the researcher's best knowledge, no previous study has scrutinized the impact of satisfaction with AI-based audit services on cultivating audit client trust in audit firms, in contrast to past research that has focused on the auditors' trust in the audit client. To bridge these gaps, this study employs a comprehensive and integrative theoretical model.

While much of the literature testing for shirking by professional athletes have used performance metrics, some works have quantified shirking in dollar terms by comparing salary to estimated marginal revenue product (MRP). However, Ordinary Least Squares (OLS) approaches to measuring shirking by comparing salary to MRP have an endogeneity problem, as salary and contract length are determined simultaneously. We test for shirking in Major League Baseball (MLB) using an MRP approach, addressing this potential endogeneity.

This paper uses instrumental variables regression to address potential endogeneity using MLB season-level player and team data from 2010 to 2017.

Using OLS regression, the impact of an additional year of guaranteed contract on shirking is estimated at approximately $1m in 2010 US dollars, and the impact of having a long-term contract is estimated at $5m, estimates comparable to those in the literature. Using instrumental variables regression, these impacts increase to $1.6m and over $9m in 2010 dollars.

Given large, causal shirking estimates, profit maximizing sports organizations should take caution when negotiating long-term contracts. These findings also have important implications for other labor market settings where workers feel job security.

To our knowledge, this is the first work testing for shirking in sports using an MRP approach which uses instrumental variables regression to address potential endogeneity.

The purpose of this paper is to provide a literature review on what is known about unpaid family carers who are at risk of or have experienced abuse from the people they provide care for and relevant policy/legal and practice responses for affected family carers.

A literature search was carried out to locate literature relating to unpaid family carers who are at risk of or have experienced abuse from the people they provide care for. This also incorporated grey literature, including policy guidance and law, to determine the existing knowledge base, gaps in practice and areas that might require further research.

The findings suggest that although carer harm is serious, it is under-researched. In addition, the unique needs of unpaid family carers who are at risk of or have experienced abuse, violence and harm from the people they provide care for are subsumed in safeguarding policy/law processes and practice under the auspices of the protection of “adults at risk” rather than the protection of “carers at risk”.

It is important that those who support unpaid family carers who are at risk of abuse and harm know about their unique safeguarding needs and concerns to offer appropriate support. It is also apparent that policy and law need to address the gap in provision relating to the unique safeguarding concerns involving the abuse of unpaid family carers by the people they provide care for. This paper is based on this literature review and not on other types of research.

The paper provides insights into what is known about the abuse of unpaid family carers by the people they provide care for, and the policy/legal and practice responses to affected unpaid family carers. It contributes to the body of knowledge on carer abuse and safeguarding carers from abuse and harm.

Developing a reliable cost estimate at the early stage of construction projects is challenging due to inadequate project information. Most of the information during this stage is qualitative, posing additional challenges to achieving accurate cost estimates. Additionally, there is a lack of tools that use qualitative project information and forecast the budgets required for project completion. This research, therefore, aims to develop a model for setting project budgets (excluding land) during the pre-conceptual stage of residential buildings, where project information is mainly qualitative.

Due to the qualitative nature of project information at the pre-conception stage, a natural language processing model, DistilBERT (Distilled Bidirectional Encoder Representations from Transformers), was trained to predict the cost range of residential buildings at the pre-conception stage. The training and evaluation data included 63,899 building permit activity records (2021–2022) from the Victorian State Building Authority, Australia. The input data comprised the project description of each record, which included project location and basic material types (floor, frame, roofing, and external wall).

This research designed a novel tool for predicting the project budget based on preliminary project information. The model achieved 79% accuracy in classifying residential buildings into three cost_classes ($100,000-$300,000, $300,000-$500,000, $500,000-$1,200,000) and F1-scores of 0.85, 0.73, and 0.74, respectively. Additionally, the results show that the model learnt the contextual relationship between qualitative data like project location and cost.

The current model was developed using data from Victoria state in Australia; hence, it would not return relevant outcomes for other contexts. However, future studies can adopt the methods to develop similar models for their context.

This research is the first to leverage a deep learning model, DistilBERT, for cost estimation at the pre-conception stage using basic project information like location and material types. Therefore, the model would contribute to overcoming data limitations for cost estimation at the pre-conception stage. Residential building stakeholders, like clients, designers, and estimators, can use the model to forecast the project budget at the pre-conception stage to facilitate decision-making.