Search

Work from home has become a new normal in this global pandemic situation. Exploration of work life balance in this new context has become imperative to deal this critical situation. This study intends to explore work life balance (WLB), family and work conflicts, work and family demands, satisfaction and challenges related with work from home and assessing their interrelationships and effects. It also measures the group differences based on demographic variables and the mediation effect of work family conflicts, and challenges of work from home in the relationship between WLB and satisfaction with work from home. The sample of the study was university teachers teaching from home to undergraduate and postgraduate classes during COVID-19. Data was collected using a self-developed questionnaire, administered online. Results suggest that majority of the respondents were balancing their lives with work from home and were satisfied with it but were facing work and family demands and conflicts while dealing family and work at a time under the same roof. The results revealed significant positive correlation between WLB and satisfaction with work from home, and negative association of, family and work conflict and demands and challenges of work from home with teachers' satisfaction and WLB. Females, single and young teachers, with more work load were facing more work demands while staying at home as compared to males, married and senior teachers with less workload. The results indicated that the work family conflict and challenges of work from home had significant effect on satisfaction of university teachers' work from home and were partially mediating the association between work-life balance and satisfaction with work from home. It was suggested that employees should make arrangements such as domestic help and rational distribution of time for both the roles to be more effective while working from home.

Prostate cancer is the most frequent cancer in men and a leading cause of cancer death. Determining a patient's optimal therapy is a challenge, where oncologists must select a therapy with the highest likelihood of success and the lowest likelihood of toxicity. International standards for prognostication rely on non-specific and semi-quantitative tools, commonly leading to over- and under-treatment. Tissue-based molecular biomarkers have attempted to address this, but most have limited validation in prospective randomized trials and expensive processing costs, posing substantial barriers to widespread adoption. There remains a significant need for accurate and scalable tools to support therapy personalization. Here we demonstrate prostate cancer therapy personalization by predicting long-term, clinically relevant outcomes using a multimodal deep learning architecture and train models using clinical data and digital histopathology from prostate biopsies. We train and validate models using five phase III randomized trials conducted across hundreds of clinical centers. Histopathological data was available for 5654 of 7764 randomized patients (71%) with a median follow-up of 11.4 years. Compared to the most common risk-stratification tool-risk groups developed by the National Cancer Center Network (NCCN)-our models have superior discriminatory performance across all endpoints, ranging from 9.2% to 14.6% relative improvement in a held-out validation set. This artificial intelligence-based tool improves prognostication over standard tools and allows oncologists to computationally predict the likeliest outcomes of specific patients to determine optimal treatment. Outfitted with digital scanners and internet access, any clinic could offer such capabilities, enabling global access to therapy personalization.