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The COVID-19 pandemic exacerbated food insecurity and insufficiency in the United States. However, the causes of food insufficiency among people with disabilities during the pandemic are not well understood. This paper examined how loneliness and household structure are associated with food insufficiency among working-age adults (ages 18–64) with disabilities during the COVID-19 pandemic. Using 2021 National Survey on Health and Disability data, we conducted logistic regression to estimate the association between loneliness, household composition, and food insufficiency. Measures of household structure, such as the number of children in the household and living with other adults, were not associated with food insufficiency during the COVID-19 pandemic. Secondary analyses found that loneliness had a strong association with food insufficiency for those who live alone. Respondents who lived alone and reported feeling lonely had the highest odds of being food insufficient during the pandemic. Our findings indicate that in addition to household structure, it is important to assess psychosocial well-being, such as measures of loneliness, when examining food insufficiency among working-age adults with disabilities.

The notion of sexual health has become a buzzword across various spheres, including the scientific, political, and social arenas. In a similar manner, discussions about the subject of disability and sexuality are commonly articulated through the lens of sexual health and “healthy sexualities.” Greater focus has been placed on issues of protection, abuse, sexually transmitted diseases, and unplanned pregnancy. Opportunities to talk about sex, desire, and pleasure is missing in this discourse. Drawing on my experience conducting studies about disability and sexuality, I interrogate the (over)reliance and unproblematized use in terms of the language of sexual health and healthy sexuality when it comes to people labeled/with intellectual disabilities.

In times of crisis, such as the Covid-19 global pandemic, journalists who write about biomedical information must have the strategic aim to be clearly and easily understood by everyone. In this study, we assume that journalistic discourse could benefit from language redundancy to improve clarity and simplicity aimed at science popularization. The concept of language redundancy is theoretically discussed with the support of discourse analysis and information theory. The methodology adopted is a corpus-based qualitative approach. Two corpora samples with Brazilian Portuguese (BP) texts on Covid-19 were collected. One with texts from a monthly science digital magazine called Pesquisa FAPESP aimed at students and researchers for scientific information dissemination and the other with popular language texts from a news Portal G1 (Rede Globo) aimed at unspecified and/or non-specialized readers. The materials were filtered with two descriptors: “vaccine” and “test.” Preliminary analysis of examples from these materials revealed two categories of redundancy: paraphrastic and polysemic. Paraphrastic redundancy is based on concomitant language reformulation of words, sentences, text excerpts, or even larger units. Polysemic redundancy does not easily show material evidence, but is based on cognitively predictable semantic association in socio-cultural domains. Both kinds of redundancy contribute, each in their own way, to improving text readability for science popularization in Brazil.

We revisit the problem of redesigning the Master in Business Administration (MBA) program, curriculum, and pedagogy, focusing on understanding and seeking to tame its “wicked problems,” as an intrinsic part and challenge of the MBA program venture, and to render it more realistic and relevant to address major problems and their consequences. We briefly review the theory of wicked problems and methods of dealing with their consequences from multiple perspectives. Most characterization of problems classifies them as simple (problems that have known formulations and solutions), complex (where formulations are known but not their resolutions), unstructured problems (where formulations are unknown, but solutions are estimated), and “wicked” (where both problem formulations and their resolutions are unknown but eventually partially tamable). Uncertainty, unpredictability, randomness, and ambiguity increase from simple to complex to unstructured to wicked problems. A redesigned MBA program should therefore address them effectively through the four semesters in two years. Most of these problems are real and affect life and economies, and hence, business schools cannot but incorporate them into their critical, ethical, and moral thinking.

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.

This chapter seeks to understand the intersection between eGovernment, social media, and digital inequalities by examining the disparate flow of information during the COVID-19 pandemic. Developed economies are increasingly transitioning to digital interfaces for information dissemination and provision of services. The authors explore the potential of, and challenges facing eGovernment by looking at the use of social media during the COVID-19 pandemic. This chapter employs a case study approach to probe the dynamics of government-initiated efforts at information dissemination through the Center for Disease Control and Prevention’s (CDC) website and social media account on Twitter. The analysis in this chapter uses NodeXL to examine communication roles played by government and non-governmental actors within this slice of the Twittersphere centered around CDC@gov. As the findings demonstrate, non-governmental actors played key roles in the dissemination of public health messaging. The authors analyze these data with an eye to the potential of social media for public health communication and extrapolate that understanding to the use of digital access and social media for the provision of accurate, official information in other circumstances. While the COVID-19 pandemic was a global health crisis, individuals and households face individual or local crises every day. This angle of vision allows the chapter to conclude with recommendations pertaining to government-led information dissemination for the public good during crisis and non-crisis situations alike. In the concluding section, the authors probe the degree to which eGovernment can also address digital inequalities including connectivity, device, and literacy gaps. The authors offer solutions needed for eGovernment initiatives in light of challenges posed by digital inequalities to ensure that digital information sharing and services are accessible to all.

Scholarly works on sex work and sex workers are mostly confined to discourses on human trafficking and the incidence of HIV/STIs among sex workers. Although crucial, this restricted focus has neglected the reality that sex workers are a diverse community, and while their challenges may appear to be linked at first glance, they differ greatly. While extensive research has been conducted on sex workers working in more open settings like brothels, hotels, and streets, there is a scarcity of research on sex workers working in more private spaces, such as, for instance, their own homes. Within the hierarchy of sex workers, home-based sex workers (HBSWs) among the indoor sex workers dominate commercial sex transactions. However, they are often overlooked due to their covert nature and invisible landscape. This chapter addresses the knowledge gap by examining the work lives and conditions of home-based female sex workers (FHBSWs) in Punjab. The study analyzes the complex lives of sex workers who use their home as both a family unit and a workplace. A detailed analysis of the risks and vulnerabilities they face in their daily lives and their coping strategies is also examined in this chapter. The study points out that although working from home may have positive outcomes for sex workers, the integration of sex work into the home environment exposes them to several challenges. Hence, the study emphasizes the need for tailoring interventions for sex workers who operate in different physical environments so that their unique needs and challenges are well addressed.

Purpose: The study aims to analyse and discuss the effect of COVID-19 on businesses. The chapter discusses the various machine learning (ML) tools and techniques, which can help in better decision making by businesses in the present world.

Need for the Study: COVID-19 has increased the role of VUCA elements in the business environment, and there is a need to address the challenges faced by businesses in such environment. ML and artificial learning can help businesses in facing such challenges.

Methodology: The focus and approach of the chapter are in the context of using artificial intelligence (AI) and ML techniques for decision making during the COVID-19 pandemic in a VUCA business environment.

Findings: The key findings and their implications emphasise the importance of understanding and implementing AI and ML techniques in business strategies during times of crisis.

Practical Implications: The chapter’s content is in the context of using AI and ML techniques during the COVID-19 pandemic and in a VUCA business environment.