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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.

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.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

This study aims to deepen the understanding of the challenges and implications entailed by deploying mobile clinics in conflict zones to reach populations affected by violence and cut off from health-care services.

This research combines an integrated literature review and an instrumental case study. The literature review comprises two targeted reviews to provide insights: one on conflict zones and one on mobile clinics. The case study describes the process and challenges faced throughout a mobile clinic deployment during and after the Iraq War. The data was gathered using mixed methods over a two-year period (2017–2018).

Armed conflicts directly impact the populations’ health and access to health care. Mobile clinic deployments are often used and recommended to provide health-care access to vulnerable populations cut off from health-care services. However, there is a dearth of peer-reviewed literature documenting decision support tools for mobile clinic deployments.

This study highlights the gaps in the literature and provides direction for future research to support the development of valuable insights and decision support tools for practitioners.

Healthcare workers’ (HCWs’) job-related high exposure to Covid-19 virus arouses fear of Covid-19 among them. Based on the Theory of Mind (ToM), the study predicts that fears will lead to negative psychological (psychological distress) and behavioral (withdrawal intentions) outcomes. ToM is also used to identify social intelligence as a means to counter fear of Covid-19 on heightened psychological distress and increased withdrawal intentions.

To investigate the study design, a sample of 262 HCWs, including doctors, nurses and technicians, were surveyed using standardized questionnaires.

As predicted, Covid-19 fear led to increased withdrawal intentions with heightened psychological distress partially mediating the relationship. The alleviating role of social intelligence on the effects of Covid-19 was supported as high social intelligence reduced HCWs’ turnover intentions, with decreased psychological distress partially mediating the relationship.

Given the universality of the Theory of Mind (ToM), the findings of this study are likely to be generalizable to all pandemics. The study results support the increased application of ToM in organizational settings and have both theoretical and practical implications for health administrators. Based on study results, health administrators are exhorted to develop ToM-based mental models to understand and deal with the fear of contagious diseases. Health administrators can also increase HCWs’ social intelligence to deal with the negative perceptual and behavioral outcomes arising from the emotions aroused by the nature of their work.

People with intellectual disability die prematurely and from avoidable causes. Innovative solutions and proactive strategies have been limited in addressing this disparity. This paper aims to detail the process of developing a risk stratification tool to identify those individuals who are higher risk of premature mortality.

This study used population health management principles to conceptualise a risk stratification tool for avoidable deaths in people with intellectual disability. A review of the literature examined the existing evidence of causes of death in people with intellectual disability. A qualitative methodology using focused groups of specialist clinicians was used to understand the factors that contributed towards avoidable deaths in people with intellectual disability. Delphi groups were used for consensus on the variables for inclusion in the risk stratification tool (Decision Support Tool for Physical Health).

A pilot of the Decision Support Tool for Physical Health within specialist intellectual disability service demonstrated effective utility and acceptability in clinical practice. The tool has also demonstrated good face and construct validity. A further study is currently being completed to examine concurrent and predictive validity of the tool.

To the best of the authors’ knowledge, this is the only study that has used a systematic approach to designing a risk stratification tool for identifying premature mortality in people with intellectual disability. The Decision Support Tool for Physical Health in clinical practice aims to guide clinical responses and prioritise those identified as at higher risk of avoidable deaths.

The COVID-19 pandemic has caused severe challenges to ethnic minorities in the UK. While the experiences of migrants are both complex and varied depending on individuals' social class, race, cultural proximity to the host country and acculturation levels, more in-depth studies are necessary to fully understand how COVID-19 affects specific migrant groups and their health. Taiwanese migrants were selected because they are an understudied group. Also, there were widespread differences in pandemic management between the UK and Taiwan, making this group an ideal case for understanding how their acculturation journey can be disrupted by a crisis.

Qualitative data were collected at two different time points, at the start of the UK pandemic (March/April 2020) and six months on (October/November 2020), to explore migrant coping experiences over time. Theoretically, the authors apply acculturation theory through the lens of coping, while discussing health-consumption practices, as empirical evidence.

Before the outbreak of the pandemic, participants worked hard to achieve high levels of integration in the UK. The pandemic changed this; participants faced unexpected changes in the UK’s sociocultural structures. They were forced to exercise the layered and complex “coping with coping” in a hostile host environment that signalled their new marginalised status. They faced impossible choices, from catching a life-threatening disease to being seen as overly cautious. Such experience, over time, challenged their integration to the host country, resulting in a loss of faith in the UK’s health system, consequently increasing separation from the host culture and society.

It is important to note that the Taiwanese sample recruited through Facebook community groups is biased and has a high level of homogeneity. These participants were well-integrated, middle-class migrants who were highly educated, relatively resourceful and active on social media. More studies are needed to fully understand the impact on well-being and acculturation of migrants from different cultural, contextual and social backgrounds. This being the case, the authors can speculate that migrants with less resource are likely to have found the pandemic experience even more challenging. More studies are needed to fully understand migrant experience from different backgrounds.

Public health policymakers are advised to dedicate more resources to understand migrants' experiences in the host country. In particular, this paper has shown how separation, especially if embraced temporarily, is not necessarily a negative outcome to be corrected with specific policies. It can be strategically adopted by migrants as a way of defending their health and well-being from an increasingly hostile environment. Migrants' home country experience provides vicarious learning opportunities to acquire good practices. Their voices should be encouraged rather than in favour of a surprising orthodox and rather singular approach in the discussion of public health management.

The paper has clear public health policy implications. Firstly, public health policymakers are advised to dedicate more resources to understand migrants' experiences in the host country. Acknowledging migrants' voice is a critical first step to contribute to the development of a fair and inclusive society. Secondly, to retain skilful migrants and avoid a future brain-drain, policymakers are advised to advance existing infrastructure to provide more incentives to support and retain migrant talents in the post-pandemic recovery phase.

This paper reveals how a group of previously well-integrated migrants had to exercise “coping with coping” during the COVID crisis. This experience, over time, challenged their integration to the host country, resulting in a loss of faith in the UK’s health system, consequently increasing separation from the host culture and society. It contributes to the understanding of acculturation by showing how a such crisis can significantly disrupt migrants' acculturation journey, challenging them to re-acculturate and reconsider their identity stance. It shows how separation was indeed a good option for migrants for protecting their well-being from a newly hostile host environment.

Travel decreased dramatically during the initial wave of the COVID-19 pandemic, only to return rapidly to prepandemic levels once the degree of fear toward the virus began to diminish among potential travelers. This USA-based 16-month repeated-measure cross-sectional survey study aims to explore the degree to which fear of COVID affected people’s decisions to stay home rather than to travel during the pandemic.

The research used survey data. An extensive data set, composed of over 9,500 respondents, collected through Mechanical Turk over a 16-month time period, was used to compare respondent fear of the pandemic both with their attitudes toward future travel and with Smith Travel Research data reflecting actual pandemic travel patterns.

The results demonstrate how fear of COVID was closely and negatively linked to both travel intentions and travel behavior.

Data were collected from US respondents only.

The findings significantly extend earlier studies and provide guidance for those studying travel consumer behavior regarding trends that should be monitored in the case of a future pandemic or other fear-inducing crisis. For hospitality and tourism managers and marketers, understanding fear as a leading indicator of future travel behavior can result in more timely promotional efforts and staffing and training decisions.

Measuring and understanding consumer fear levels as this relates to travel decisions can help in the future to adjust the message that is sent to the public, perhaps reducing the amount of travel taken during periods when this is unwise and or unsafe.

This paper extends previous work that had been based upon cross-sectional reviews, providing a broader and more valuable study of an important and timely consumer behavior travel topic.