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This paper explores the potential that block teaching offers to enhance employability in the context of large-scale classes. It suggests that block teaching, with its condensed structure, necessitates curriculum innovation, fosters participatory learning and peer-to-peer networking, and has been shown to increase student focus and enhance engagement and attainment, especially amongst diverse learners. As these are the same challenges that large-scale teaching faces, it is proposed that intensive modes of delivery could be scaled up in a way that may help to mitigate such problems as cohorts in business schools continue to increase in size.

The paper is based on secondary research and provides an overview of literature that looks at block teaching, followed by that which explores the challenges of large-scale teaching contexts. It compares and contrasts the gaps in both to suggest a way that they could be combined.

The paper provides key insights into changes in the contemporary landscape of teaching within UK business schools, which have seen increasingly large cohorts and draws out the key strengths of intensive modes of delivery, which include helping students to time manage effectively, encouraging curriculum innovation and the creation of participatory learning opportunities as well as providing closer personal relationships between students and staff. Outlining some of the well-documented issues that can arise when teaching larger cohorts, the paper suggests that scaling up blocked delivery may offer a new way help to overcome them.

Because of the chosen research approach, the research results are subject to generalisation. Therefore, researchers are encouraged to test the proposed propositions in large-scale teaching scenarios.

This paper includes implications for the development of innovative modes of teaching in the context of large cohorts, an experience that is increasingly common amongst British business schools and beyond.

This paper brings together two bodies of literature for the first time – that of intensive modes of teaching and that focuses on large-scale teaching contexts – for the first time to show how the former may help to overcome some of the key issues arising in the latter.

Organizational scholars studying compensation often place an emphasis on certain employee groups (e.g., executives). Missing from this discussion is research on the compensation systems for low-wage jobs. In this review, the authors argue that workers in low-wage jobs represent a unique employment group in their understanding of rent allocation in organizations. The authors address the design of compensation strategies in organizations that lead to different outcomes for workers in low-wage jobs versus other workers. Drawing on and integrating human resource management (HRM), inequality, and worker literatures with compensation literature, the authors describe and explain compensation systems for low-wage work. The authors start by examining workers in low-wage work to identify aspects of these workers’ jobs and lives that can influence their health, performance, and other organizationally relevant outcomes. Next, the authors explore the compensation systems common for this type of work, building on the compensation literature, by identifying the low-wage work compensation designs, proposing the likely explanations for why organizations craft these designs, and describing the worker and organizational outcomes of these designs. The authors conclude with suggestions for future research in this growing field and explore how organizations may benefit by rethinking their approach to compensation for low-wage work. In sum, the authors hope that this review will be a foundational work for those interested in investigating organizational compensation issues at the intersection of inequality and worker and organizational outcomes.

Nearly 75% of EU buildings are not energy-efficient enough to meet the international climate goals, which triggers the need to develop sustainable construction techniques with high degree of resilience against climate change. In this context, a promising construction technique is represented by ventilated façades (VFs). This paper aims to propose three different VFs and the authors define a novel machine learning-based approach to evaluate and predict their energy performance under different boundary conditions, without the need for expensive on-site experimentations

The approach is based on the use of machine learning algorithms for the evaluation of different VF configurations and allows for the prediction of the temperatures in the cavities and of the heat fluxes. The authors trained different regression algorithms and obtained low prediction errors, in particular for temperatures. The authors used such models to simulate the thermo-physical behavior of the VFs and determined the most energy-efficient design variant.

The authors found that regression trees allow for an accurate simulation of the thermal behavior of VFs. The authors also studied feature weights to determine the most relevant thermo-physical parameters. Finally, the authors determined the best design variant and the optimal air velocity in the cavity.

This study is unique in four main aspects: the thermo-dynamic analysis is performed under different thermal masses, positions of the cavity and geometries; the VFs are mated with a controlled ventilation system, used to parameterize the thermodynamic behavior under stepwise variations of the air inflow; temperatures and heat fluxes are predicted through machine learning models; the best configuration is determined through simulations, with no onerous in situ experimentations needed.

A growing body of evidence suggests that experiences of discrimination may affect physical and mental health through multiple pathways. This paper aims to characterize the extent of everyday perceived discrimination among Brazilian and Cape Verdean immigrant adults in Portugal, to identify its forms and analyze its association with mental health.

This study draws on data from the EQUALS4COVID19 (Equity in health in times of pandemic) project that implemented a cross-sectional survey combining online and face-to-face questionnaires for data collection between February and November 2022.

Women were less likely than men to report no discrimination experiences and more likely to report combined bases of discrimination. While Brazilian women were more likely than Cape Verdean counterparts to report gender and nationality-based discrimination, Cape Verdean women and men were more likely to report experiencing race-based discrimination. Gender (being identified as a woman) and length of stay in Portugal were the main predictors of depression, while resilience and perceived social support were protective factors. Participants with higher levels of resilience showed a significantly diminished association between perceived discrimination and depressive symptoms.

This study highlights the need for further research on the interlocking influences of gender, race, nationality and other structures of power, on health and illness to increase our understanding of what would help meet the specific needs of migrants’ mental health and improve equitable health care.

The findings on the multiple and intersectional discrimination perceived by the Brazilian and Cape Verdean populations in Portugal illustrated the ways mental health can be affected by social structures, such as gender and ethnic hierarchies, and can be used to inform the relevance to design and implement programs on combating individual and institutional discrimination and improving the rights of all people.

The purpose of this study is to gain a better understanding of the conditions and motivations that influence teachers to adopt innovations.

Using Diffusion of Innovation theory (Rogers, 2003) and Self-Determination theory (Ryan and Deci, 2017), data from two surveys (n = 568; n = 108) and qualitative follow-up interviews of Early Adopter teachers (n = 16) were triangulated to discern relationships among their identification as Early Adopters, satisfaction of their basic psychological needs (BPN) and their implementation of an educational innovation.

Early Adopters had a positive and statistically significant relationship with the implementation of the innovation. Satisfaction of teachers’ BPN had the largest impact on innovation.

The findings are preliminary and based on a small sample size of teachers. Reliability of the measure of BPN was not as high as the standard, but it did have the largest impact on implementation. Additional studies should explore the connections among Early Adopter teacher motivation, leadership and the satisfaction of their BPN.

School leaders should leverage the influence of Early Adopters to support innovation, and they should provide additional time, training and resources to supports teachers’ BPN.

This study examines how to identify and support Early Adopter teachers as enablers of change within schools. We know of no other studies that have used both Diffusion of Innovation theory and Self-Determination theory to understand the motivations of Early Adopter teachers.

COVID-19 is an infectious disease caused by the SARS-CoV-2 virus. Considering the restricted and enclosed nature of prisons and closed environments and the prolonged and close contact between individuals, COVID-19 is more likely to have a higher incidence in these settings. This study aims to assess the prevalence of COVID-19 among prisoners.

Papers published in English from 2019 to July 7, 2023, were identified using relevant keywords such as prevalence, COVID-19 and prisoner in the following databases: PubMed/MEDLINE, Scopus and Google Scholar. For the meta-analysis of the prevalence, Cochrane’s Q statistics were calculated. A random effect model was used due to the heterogeneity in COVID-19 prevalence across included studies in the meta-analysis. All analyses were performed in STATA-13.

The pooled data presented a COVID-19 prevalence of 20% [95%CI: 0.13, 0.26] and 24% [95%CI: 0.07, 0.41], respectively, in studies that used PCR and antibody tests. Furthermore, two study designs, cross-sectional and cohort, were used. The results of the meta-analysis showed studies with cross-sectional and cohort designs reported 20% [95%CI: 0.11, 0.29] and 25% [95%CI: 0.13, 0.38], respectively.

Through more meticulous planning, it is feasible to reduce the number of individuals in prison cells, thereby preventing the further spread of COVID-19.