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This chapter provides an overview on the traditions and values of teaching students with traumatic brain injury (TBI). First, we discuss the prevalence, identification, and characteristics associated with TBI and how those characteristics affect learning, behavior, and daily life functioning. Next, we focus on instructional and behavioral interventions used in maintaining the traditions in classrooms for working with students with TBI. Findings from a review of the literature conclude that there are no specific academic curriculums designed specifically for teaching students with TBI; however, direct instruction and strategy instruction have been shown to be effective educational interventions. Current research on students with TBI is predominately being conducted in medical centers and clinics focusing on area of impairments (e.g., memory, attention, processing speed) rather than academic achievement and classroom interventions. Finally, we conclude with a list of accommodations and a discussion of recommendations for future work in teaching students with TBI.

Institutional abuse is a worldwide phenomenon with the UK also subject to several high-profile abuse scandals perpetuated on people with learning disabilities and/or mental health conditions living within institutional settings. This study aims to provide a broad perspective of safeguarding practices within institutional care to inform practice and service development in this area.

A narrative overview was undertaken of a range of empirical evidence, discussion papers, enquiry reports, reports from regulatory bodies and professional guidance to explore safeguarding practices within institutional care for individuals with learning disabilities and/or mental health conditions.

A range of literature was identified that exposed and explored abuse in this context. Three key themes were identified: failings within institutional care; safeguarding issues and concerns; and good practice within institutional care. Whilst guidance is available, standards are explicit and protocols facilitate improvement potential in this area, a consistent message was that statutory recommendations for reform have not been effective.

This paper provides an important resource for practitioners and service providers involved in institutional care. An accessible overview of both the empirical evidence and grey literature on adult safeguarding within institutional settings is provided, along with a range of standards and resources that specify practice in these settings.

Three years after the COVID epidemic and the measures put in place by governments, the authors still cannot measure the full impact of them on the well-being and mental health of adolescents. This population was particularly impacted by this crisis, and some subgroups of young people, such as those from migration backgrounds, have been confronted with additional challenges. This paper aims to explore and describe the perspective and experience of migrant adolescents during the lockdown period

The authors performed 13 semi-structured interviews with migrant adolescents (nine females, median age 16) at the pediatric policlinic of Lausanne University Hospital in Switzerland between November 2020 and January 2021. Participants had been living in Switzerland for an average of 2.3 years. Three of them were staying in a refugee reception centre. A thematic content analysis was carried out to extract themes and topics.

Participants had difficulty understanding information about COVID-19 in general. Remote learning was described as stressful due to various factors and lockdown had an impact on their future plans, such as finding an apprenticeship. Some young people were already socially isolated, but families were generally supportive. They expressed particular concerns, such as the family’s financial situation and the difficulty of living in refugee reception centres.

In the event of further lockdown, special attention must be paid to these adolescents to ensure their proper development and integration. Comprehensive follow-up of this population during and after the pandemic is essential.

This study provides a better understanding of the pandemic experience of migrant adolescents and underlines their difficulties.

The key learning from this case study include the following: first, there are various leadership types that leaders can exhibit, such as servant leadership and transformational leadership, and an individual’s leadership style can be evaluated by analysing his/her traits and behaviours. Second, decision-making is much more critical for leaders than for anyone else, and there are different approaches, such as rational and intuitive decision-making, that leaders can apply when making a decision. Third, in male-dominated work environments such as the sports sector, female executives should carefully weigh the risks and opportunities of leadership positions while being promoted.

The UPS Sports and Culture Club was founded in 2003 by Haluk Ündeğer in Zeytinburnu district, one of the most dangerous neighbourhoods in Istanbul that had a bad reputation for being high on crime and drugs. The club’s main goal was to train children from disadvantaged groups to develop a career in sports. Shortly after the club’s founding, Semra Demirer, a physical education teacher who had devoted her life to children’s physical, cultural and personal development, crossed paths with the UPS Club. In 2004, Demirer started to work at the UPS Sports and Culture Club as the general coordinator. She played an important role in the growth and development of many children over the years and helped raise very talented athletes such as Simge Aköz. In 2020, on the heels of financial and administrative difficulties, the club was at the risk of being shut down. Hence, Demirer grappled with the decision of whether to share this information with the employees and players in the club. She deeply considered how she could overcome the conflict between transparency and confidentiality she was experiencing.

The case study is suitable for undergraduate students.

Teaching notes are available for educators only.

CSS 6: Human Resource Management.

This study aims to investigate the prevalence and characteristics of dysphagia among adults with intellectual disabilities (ID) referred to speech and language therapy for swallow assessment, providing information on the demographic characteristics, referral trends, co-occurring health conditions and reasons for referrals highlighting the complex health-care needs of this population.

This study used a standardised patient data extraction method over a six-month period involving 74 adults with ID referred to speech and language therapy for swallow assessment.

This study revealed a high prevalence of dysphagia among adults with ID referred to speech and language therapy for swallow assessment. Increasing age and severity of ID were associated with an increased likelihood of swallowing difficulties. Co-occurring health conditions such as mobility difficulties, epilepsy and gastrointestinal conditions were prevalent, suggesting that adults with ID and swallowing difficulties are often living with complex health conditions. Choking incidents and hospital admissions were primary reasons for referral.

This study stresses the pressing need for strategies to mitigate risks associated with choking incidents and hospital admission among this vulnerable population. Possible limitations include a reliance on referral and the focus being on a single service over a short period which may limit generalisation to the wider ID population.

This study emphasises the need to understand each person’s unique profile of health needs and the value of a specialised speech and language therapy service.

The importance of increasing awareness among caregivers and medical experts is highlighted.

The findings underscore the importance of tailored assessment, caregiver involvement and heightened interdisciplinary awareness to effectively manage dysphagia in individuals with ID.

The objective of this research is to propose an immersive framework that integrates virtual reality (VR) technology with directives international safety training certification bodies to enhance construction safety training, which eventually leads to safer construction sites.

The adopted methodology combines expert insights and experimentation to maximize the effectiveness of construction safety training. The first step was identifying key considerations for VR models such as motion sickness prevention and adult learning theories. The second step was developing a game-like VR model for safety training, with multiple hazards and scenarios based on the considerations of the previous step. After that, safety experts evaluated the model and provided valuable feedback on its alignment with international safety training practices. Finally, the developed model is tested by senior students, where the testing format followed the Institution of Occupational Safety and Health (IOSH) working safely exam structure.

An advanced immersive VR safety training model was developed based on extensive lessons learned from the literature, previous work and psychology-informed adult learning theories. Model testing – through focus groups and hands-on experimentation – demonstrated significant benefit of VR in upgrading and complementing traditional training methods.

The findings presented in this paper make a significant contribution to the field of safety training within the construction industry and the broader context of immersive learning experiences. It also fosters further exploration into immersive learning experiences across educational and professional contexts.

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

The aim of this paper is to provide an overview of a novel offender personality disorder (OPD) higher education programme and the research evaluation results collected over a three-year period. Data from Phase 1 was collected from a face-to-face mode of delivery, and Phase 2 data collected from the same programme was from an online mode of delivery because of the COVID-19 pandemic.

In Phase 1, three modules were developed and delivered in a fully face-to-face format before the pandemic in 2019–2020 (n = 52 student participants). In 2020–2021 (n = 66 student participants), training was adapted into a fully online mode of delivery in Phase 2. This mixed-methods study evaluated participant confidence and compassion. Pre-, post- and six-month follow-up questionnaires were completed. Qualitative interviews were conducted across both phases to gain in-depth feedback on this programme (Phase 1: N = 7 students, Phase 2: N = 2 students, N = 5 leaders). Data from Phase 1 (face-to-face) and Phase 2 (online) are synthesised for comparison.

In Phase 1 (N = 52), confidence in working with people with personality disorder or associated difficulties improved significantly, while compassion did not change. In Phase 2 (N = 66), these results were replicated, with statistically significant improvements in confidence reported. Compassion, however, was reduced in Phase 2 at the six-month follow-up. Results have been integrated and have assisted in shaping the future of modules to meet the learning needs of students.

Further research into the impact of different modes of delivery is important for the future of education in a post-pandemic digitalised society. Comparisons of blended learning approaches were not covered but would be beneficial to explore and evaluate in the future.

This comparison provided informed learning for consideration in the development of non-related educational programmes and, hence, was of use to other educational providers.

This paper provides a comparison of a student-evaluated training programme, thus providing insights into the impact of delivering a relational-focused training programme in both face-to-face and online distance learning delivery modes. From this pedagogic research evaluation, the authors were able to derive unique insights into the outcomes of this programme.

Consequence prediction is an emerging topic in safety management concerning the severity outcome of accidents. In practical applications, it is usually implemented through supervised learning methods; however, the evaluation of classification results remains a challenge. The previous studies mostly adopted simplex evaluation based on empirical and quantitative assessment strategies. This paper aims to shed new light on the comprehensive evaluation and comparison of diverse classification methods through visualization, clustering and ranking techniques.

An empirical study is conducted using 9 state-of-the-art classification methods on a real-world data set of 653 construction accidents in China for predicting the consequence with respect to 39 carefully featured factors and accident type. The proposed comprehensive evaluation enriches the interpretation of classification results from different perspectives. Furthermore, the critical factors leading to severe construction accidents are identified by analyzing the coefficients of a logistic regression model.

This paper identifies the critical factors that significantly influence the consequence of construction accidents, which include accident type (particularly collapse), improper accident reporting and handling (E21), inadequate supervision engineers (O41), no special safety department (O11), delayed or low-quality drawings (T11), unqualified contractor (C21), schedule pressure (C11), multi-level subcontracting (C22), lacking safety examination (S22), improper operation of mechanical equipment (R11) and improper construction procedure arrangement (T21). The prediction models and findings of critical factors help make safety intervention measures in a targeted way and enhance the experience of safety professionals in the construction industry.

The empirical study using some well-known classification methods for forecasting the consequences of construction accidents provides some evidence for the comprehensive evaluation of multiple classifiers. These techniques can be used jointly with other evaluation approaches for a comprehensive understanding of the classification algorithms. Despite the limitation of specific methods used in the study, the presented methodology can be configured with other classification methods and performance metrics and even applied to other decision-making problems such as clustering.

This study sheds new light on the comprehensive comparison and evaluation of classification results through visualization, clustering and ranking techniques using an empirical study of consequence prediction of construction accidents. The relevance of construction accident type is discussed with the severity of accidents. The critical factors influencing the accident consequence are identified for the sake of taking prevention measures for risk reduction. The proposed method can be applied to other decision-making tasks where the evaluation is involved as an important component.