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This paper applies concepts from organizational theory as well as physics to elucidate the role of time in the US education system’s efforts to recuperate from the pandemic. This paper contributes to an important body of work focusing on implementation of reform efforts in education that use time in innovative ways.

The COVID-19 pandemic disrupted time in educational organizations and, thus, for educators and students. Time has been a vital tool for educational reform, yet many applications of organizational theory and literature on educational change neglect to underscore its importance. The authors explore resources, guidelines and practices related to time employed to recuperate from pandemic-related disruptions to schooling.

The authors discuss three cases in which time has been utilized to recover from the COVID-19 pandemic: (1) accelerated learning; (2) extended time; and (3) redeveloped professional learning. For each case, the authors demonstrate how time has been conceptualized and how leaders are stretching the space-time of schooling to provide resources and learning opportunities to students and educators.

This article describes how district and school leaders can draw on their agency to reshape time-use in educational organizations.

This article advances an innovative framework demonstrating the importance of time in educational change. The authors also portray innovative models that provide time for students to receive an array of responsive, equity-centered, academic and SEL opportunities and for educators to collaborate, continuing their own development amid the ever-shifting Covid-context.

Clinical staff working in mental health services experience high levels of work-related stress, burnout and poor well-being. Increased levels of stress, burnout, depression and anxiety and poorer mental well-being among health-care workers are associated with more sick days, absenteeism, lower work satisfaction, increased staff turnover and reduced quality of patient care. Virtual reality (VR) relaxation is a technique whereby experiences of pleasant and calming environments are accessed through a head-mounted display to promote relaxation. The purpose of this paper is to describe the design of a study that assesses the feasibility and acceptability of implementing a multi-session VR relaxation intervention amongst mental health professionals, to improve their relaxation levels and mental well-being.

The study follows a pre–post-test design. Mental health staff will be recruited for five weeks of VR relaxation. The authors will measure the feasibility and acceptability of the VR relaxation intervention as primary outcomes, alongside secondary outcomes evaluating the benefits of VR relaxation for mental well-being.

The study aims to recruit 20–25 health-care professionals working in both inpatient and specialist community mental health settings.

Research indicates the potential of VR relaxation as a low-intensity intervention to promote relaxation and reduce stress in the workplace. If VR relaxation is shown to be feasible and acceptable, when delivered across multiple sessions, there would be scope for large-scale work to investigate its effectiveness as an approach to enable health-care professionals to de-stress, relax and optimise their mental well-being. In turn, this may consequently reduce turnover and improve stress-related sick leave across health-care services.

Little is known about low-income immigrant parents’ health-related quality of life (HRQoL) associated with their parenting. The purpose of this paper is to examine low-income immigrant parents’ HRQoL, depression and stress.

In the spring of 2015, English speaking and Spanish speaking low-income uninsured immigrant parents utilizing a free clinic (N=182) completed a self-administered survey using standardized measures of parental HRQoL, stress and depression.

Immigrant parents’ HRQoL related to parenting was lower than general primary care patients. Higher levels of depression and stress were associated with lower levels of parental HRQoL and family functioning. Spanish speakers were significantly more likely to worry about their child’s health or future compared to English speakers.

While both English and Spanish speaking immigrant parents may need assistance addressing the health-related needs of their child, Spanish speakers may be a target audience for outreach programs. It is possible that by improving the health of their child, immigrant parents may see improvement in their own HRQoL and reductions in their levels of stress and depression. Future research should develop parenting classes for low-income immigrant parents targeting the potential health needs of their children, and assess the efficacy of the classes in improving child health and parental HRQoL.

Artificial intelligence (AI) has started to receive attention in the field of digital entrepreneurship. However, few studies propose AI-based models aimed at assisting entrepreneurs in their day-to-day operations. In addition, extant models from the product design literature, while technically promising, fail to propose methods suitable for opportunity development with high level of uncertainty. This study develops and tests a predictive model that provides entrepreneurs with a digital infrastructure for automated testing. Such an approach aims at harnessing AI-based predictive technologies while keeping the ability to respond to the unexpected.

Based on effectuation theory, this study identifies an AI-based, predictive phase in the “build-measure-learn” loop of Lean startup. The predictive component, based on recommendation algorithm techniques, is integrated into a framework that considers both prediction (causal) and controlled (effectual) logics of action. The performance of the so-called active learning build-measure-predict-learn algorithm is evaluated on a data set collected from a case study.

The results show that the algorithm can predict the desirability level of newly implemented product design decisions (PDDs) in the context of a digital product. The main advantages, in addition to the prediction performance, are the ability to detect cases where predictions are likely to be less precise and an easy-to-assess indicator for product design desirability. The model is found to deal with uncertainty in a threefold way: epistemological expansion through accelerated data gathering, ontological reduction of uncertainty by revealing prior “unknown unknowns” and methodological scaffolding, as the framework accommodates both predictive (causal) and controlled (effectual) practices.

Research about using AI in entrepreneurship is still in a nascent stage. This paper can serve as a starting point for new research on predictive techniques and AI-based infrastructures aiming to support digital entrepreneurs in their day-to-day operations. This work can also encourage theoretical developments, building on effectuation and causation, to better understand Lean startup practices, especially when supported by digital infrastructures accelerating the entrepreneurial process.